bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Prediction for Pollutant Emissions of Coal-fired Power Plant Based on CNN-LSTM-Attention
Authors: Liu Qing , Zhang Yongzhong 
Published: October 2024

Abstract
The prediction accuracy of pollutant emissions by using traditional modeling methods is unsatisfactory in dynamic conditions. To overcome the problem, data-driven modeling was introduced to build the dynamic model of pollutant emissions of power plants in this paper. Combining with the running data of a 300MW circulating fluidized bed (CFB) unit, the dynamic prediction models of SO2 and NO
x
emissions were established respectively by using conventional neural network-long short-term memory and attention mechanism (CNN-LSTM-Attention). Moreover, LSSVM, LSTM and CNN-LSTM were introduced for comparison to demonstrate the superiority of CNN-LSTM-Attention model respectively. Simulation results indicate that model can imitate change trend of actual data with high accuracy over a long period of time. Compared with LSSVM, LSTM and CNN-LSTM, the proposed model has better modeling performance under different load conditions. This work provides certain guidance for the application of deep learning in the industrial field.
Attention Pyramid Dilated Region-based Model for Metallurgical Defect Detection
Authors: Cheng Danpeng , Sha Wuxin , Xu Zuo , Huang Lixin , Zhang Zhendong , Du Yunpeng , Zhu Zhihua , Cao Yuan-Cheng 
Published: October 2024

Abstract
Defect recognition is the key to realizing automatic visual inspection and quality control in intelligent manufacturing. Deep learning (DL) has been widely applied to locate damaged areas in images, characterize impurities of materials and further analyze the quality of products. However, automatic defect detection by DL in practical industrial applications is still a challenge due to the lack of sufficient datasets and appropriate recognition methods. Here, a novel Attention Pyramid Dilated Region-based Convolutional Neural Network (ADRCNN) is proposed to realize the multi-scale defect recognition and pixel-level instance segmentation of metallographic images. We also collected a dataset containing 900 images of commercial A356 aluminum alloy with different casting defects. The ADRCNN model is evaluated on our dataset with an average detection accuracy of 87.2% and 93.3% on validation and test sets respectively. To address the overfitting problem, a pretraining fine-tuning strategy is implemented by using the pretraining weights from large-scale datasets and temporarily freezing the weights of the backbone network during the training process. The experimental results show that the proposed model can achieve satisfactory defect segmentation in metallographic images, promoting the development of intelligent manufacturing in alloy industries.
Estimation of Carbon Stock in Chinese Fir Stands Based on Stacking Ensemble Learning and LSTM Models
Authors: Wang Zhiyuan , Shen Wanling , Chen Lingyan , Xiao Jiawen , Zhang Chaofei , Zhang Shanwei , Xu Zhanghua , Liu Jinfu 
Published: October 2024

Abstract
Forest carbon sinks, a critical component of the global carbon cycle, constitute nearly half of the total terrestrial carbon pool. This study employed correlation analysis and factor effect analysis to quantify the influences of various factors on the volume of Chinese fir stands. A novel modeling framework was developed using the stacking ensemble learning and LSTM (LongShort-TermMemory) models. This framework incorporated diverse base learners, including XGBoost, Adaboost, KNN, and DT, which are intelligently ensemble via GBDT as a meta learner. The model was further optimized by comparing activation functions and optimizers, with ReLU selected as the activation function and Adam as the optimizer. Model accuracy was evaluated using RMSE, MAE, and R2 metrics, significantly enhancing its learning ability and generalization performance. The findings are as follows: (1) Stand stocking showed strong positive correlations with depression, age group, average diameter at breast height (ADBH), average tree height (ATH), slope, and elevation. Conversely, it exhibited significant negative correlations with origin, stand density, and slope position. In investigating Chinese fir growth on slopes, no significant growth differences were observed between downslopes and midslopes; however, both differed significantly from upper slopes. (2) The stacking ensemble learning method constructed here surpassed all existing single models in terms of estimation and assessment indices, demonstrating superior comprehensive performance. (3) Among the LSTM models, Adam-LSTM performed the best (R2=0.844), followed by Sigmoid-LSTM (R2=0.656), while the RMSprop-LSTM model performed the worst (R2=0.618). Combined with artificial intelligence methods, our optimized carbon stock estimation model can help to improve the ability of forest land management and provide a theoretical basis for the scientific management of forest areas.
VIEPred: Predicting Viral Immune Evasion with Multimodal Representation Learning
Authors: Liang Banqing , Wang Na , Huang Hao , Hou Yijie , Qiang Xiaoli 
Published: October 2024

Abstract
Predicting coronaviral immune evasion is crucial for identifying and responding to new COVID-19 variants in advance, thereby optimizing vaccine development and public health strategies to prevent further outbreaks. Hence, we present VIEPred, a model designed to predict mutations in every amino acid of the RBD. However, protein representation learning is a challenge task due to the hierarchical structure of proteins. Our method uses a sequence encoder based on dilated convolutions and attention mechanisms to generate sequence embeddings and a structure encoder employing geometric vector perceptrons (GVP) and graph neural networks (GNN) to produce protein structure representations. These features are combined using a cross-attention fusion module and subsequently processed through a fully connected layer to output the probability of viral immune evasion. Extensive ablation studies led to an optimal model with ACC of 88.4% and AUC of 86.8%. Compared to existing prediction methods, VIEPred demonstrates superior predictive performance across most evaluation metrics for both SARS-CoV-2 and influenza viruses.
Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion
Authors: Li Yue , Huang Boyang , Xiao Xiaobing , Liu Anjiang , Jin Xing 
Published: October 2024

Abstract
Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.
Expression Recognition Based on Visual Transformers with Novel Attentional Fusion
Authors: Feng Boju , Zhang Hongling 
Published: October 2024

Abstract
Facial Expression Recognition (FER) encounters considerable obstacles in practical settings, which are attributed to factors such as occlusions, a range of head positions, facial shape alterations, and motion blur under specific limitations. Despite notable advancements in the automation of FER throughout the past several decades, the majority of prior studies have concentrated on FER within the confines of a laboratory environment. Nonetheless, the complexity of FER is significantly heightened by real-world challenges, including but not limited to occlusions and varied head poses, which lead to areas with insufficient information and intricate backgrounds. As an alternative to earlier CNN-based techniques, we suggest that it is not only viable but also advantageous to convert facial images into a series of visual terms and to tackle expression recognition from a holistic viewpoint. Consequently, we have developed the Visual Transformer with Feature Fusion to confront the challenges of FER in uncontrolled conditions, a process that is divided into two principal stages. Initially, we have put forth the Attention Selective Fusion (ASF) concept to capitalize on the two distinct sets of feature maps produced by a pair of CNN branches. ASF integrates global and local attention mechanisms to synthesize various pieces of discriminative information from these maps. Subsequently, these merged feature maps are transformed into a sequence of visual terms by a process of flattening and projection. Following this, taking cues from the achievements of Transformers in the field of natural language processing, we advocate for the employment of global self-attention to delineate the interrelations among the visual terms. Our proposed strategy has been rigorously tested across three public datasets known for their uncontrolled FER scenarios (RAF-DB, FERPlus, and AffectNet). The results from our comprehensive experiments indicate that our technique surpasses other existing methods, with an accuracy rate of 88.14% on RAF-DB, 88.81% on FERPlus, and 61.85% on AffectNet, thereby establishing new benchmarks in the field. Moreover, the cross-dataset evaluation conducted on the CK+ dataset affirms the robust generalizability of our proposed approach.
Finding Zero Correlation Linear Hulls Using Quantum Bernstein-vazirani Algorithm
Authors: Xie Huiqin , Zhao Zhangmei 
Published: October 2024

Abstract
Facing the threat of quantum computing to cryptography schemes, it is urgent to study the applications of quantum algorithms to cryptanalysis and evaluate the security of cryptographic schemes in a quantum computing environment. To do this, it is necessary to analyze the ability of cryptanalysis methods, such as integral and linear attacks, when combined with quantum algorithms. In this article, we investigate the properties of block ciphers with SPN structure and their security against a quantum adversary and apply the Bernstein-Vazirani algorithm to zero correlation linear attacks. Based on the relation between linear approximates of SPN ciphers and the dual cipher’s linear structures, we proposed a quantum algorithm that can find linear hulls with zero correlation of SPN ciphers. We show the validity of the proposed quantum algorithm and estimate the corresponding computational complexity. Implementing our quantum algorithm only costs polynomial qubits and quantum gates.
Open-set Communication Scene Recognition Based on Metric Learning
Authors: Wang Yankun , Guo Dengke , Ma Dongtang , Zhang Xiaoying , Xiong Jun 
Published: October 2024

Abstract
In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.
Comparison of the experience of using digital games in mathematics education in Ukraine and Israel
Authors: Voievoda A L , Klochko O V , Gurevych R S , Konoshevskyi O L 
Published: October 2024

Abstract
The study examined the opinion of school students regarding the expediency and possibilities of using digital mathematical games in the process of teaching mathematics, and also carried out a comparative analysis of the results of the research conducted in Ukraine and Israel. The issue of the distribution of digital mathematical games in Ukraine and Israel was studied, and it was found out which digital mathematical games are the most popular in the process of teaching mathematics to students in schools in these countries. It was found that the vast majority of students from these countries played digital mathematical games. In the study of students’ interest in playing digital mathematical games, the questions were whether students played digital mathematical games, whether their mathematics teachers used digital mathematical games in mathematics lessons, whether students would like to use digital mathematical games when learning mathematics at school. Also, most students responded that their teachers used digital math games in math lessons and would like their teachers to continue using them. However, despite a relatively high percentage of students who are interested in using digital math games in math lessons, there are students who consider traditional math lessons to be more appropriate. It was established that there is a statistically significant difference between the results of a survey of schoolchildren in Ukraine and Israel regarding the expediency of using mathematical digital games in teaching mathematics: with a probability of 95% more surveyed schoolchildren in Ukraine (9.6%) are supporters of traditional teaching of mathematics (without the use of digital games), compared to 2.4% of surveyed schoolchildren in Israel. The statistical significance of the differences in the answers of students of the schools of these countries was checked using the ϕ*-Fisher criterion. As a result of the research, it was found that the interest of schoolchildren of both countries in the use of digital mathematical games in the learning process is growing. Almost half of the respondents in these countries expressed the opinion that they would be interested in learning more about digital math games and trying to play them. However, there remain a number of unresolved important issues related to the methodology of this process, including didactic ones.
Measurement of ascorbic acid samples’ optical rotation via an improvised polarimeter for purity assessment
Authors: Felicidario R J M , delos Santos R M 
Published: October 2024

Abstract
The polarization of light finds practical application in physics and chemistry through the optical activity phenomenon, where polarimeters play a crucial role. This research builds on the improvised polarimeter designed by Kvittingen and Sjursnes [1], implemented with relevant modifications, to measure optical rotations of over-the-counter ascorbic acid samples. The study aims to assess the purity of two brands of ascorbic acid through polarimetry, comparing the calculated specific rotation with the literature values and supplementing the characterization with melting point determination. The constructed polarimeter, assembled using Lego bricks, provides an affordable alternative for educational purposes, addressing the challenges observed in the accessibility of commercial polarimeters for classroom demonstrations. The methodology encompasses pre-experiment steps involving polarimeter construction, the experiment utilizing polarimetry and complementary melting point determination, and post-experiment analysis to determine specific rotation from the measured optical rotations. Results indicate that Brand X exhibited specific rotations close to theoretical values, inferring high purity. Conversely, Brand Y shows significant deviations, suggesting potential impurities. These conclusions are supported by melting point data. The comprehensive approach combining polarimetry and melting point determination enhances the reliability of purity assessments, showcasing the effectiveness of the improvised polarimeter in practical applications.
Cognitive aspects of interaction in the “Human — Artificial Intelligence” system
Authors: Fedorets V M , Klochko O V , Tverdokhlib I A , Sharyhin O A 
Published: October 2024

Abstract
The article, based on empirical and theoretical research, reveals the phenomenology of transformations of the human cognitive sphere when interacting with artificial intelligence. The analysis of the indicated changes in the cognitive sphere is carried out on the basis of the “Concept of cognitive multi-channel Human-Computer interaction” developed by us. The essence of this concept is that the interaction of the cognitive sphere of human and artificial intelligence is implemented on the basis of the actualization and formation of typical cognitive phenomena. These phenomena are considered systemically and multifunctionally, namely as relatively independent cognitive: types of interactions, stages, strategies, channels, ontologies. Within the conceptual and substantive framework of this concept, we distinguish the following types of cognition (channels, strategies, etc.): I – orientational-cognitive; II – subject-cognitive; III – communicative and cognitive; IV – cognitive and analytical; V – cognitive and hermeneutic; VI-cognitive-ontological; VII – cognitive and creative. The identification of the indicated types of cognitive interactions is aimed at its representation as a complex, dynamic, multidimensional, multichannel intellectual system, the features of which are significant for educational and sociocultural practices, as well as for further development of artificial intelligence technologies, including its functional orientation and specificity, ergonomics, architecture, design and features of the interface. A study was conducted among students of higher education institutions aimed at determining the cognitive specificity (structure) of interaction in the “Human – Artificial Intelligence” system. Based on the analysis of the results of the distribution of answers for each of the test questions and the interpretation of the results of the cluster analysis (the Canopy algorithm was used), the dominance of the “I – orientational-cognitive” type of interactions was determined, which indicates a rather significant but initial interest in artificial intelligence technologies. There is also a relatively even distribution of all other types of cognitive interactions. The above reveals the novelty and innovation of artificial intelligence technology. This correlates with the respondents having developed different types of cognition, namely: orientational, analytical-synthetic, conceptual, interpretive, ontological, creative thinking, and corresponding intellectual intentions and motivation to use artificial intelligence tools in various spheres of activity.
Application of artificial intelligence in higher education institutions for developing soft skills of future specialists in the sphere of information technology
Authors: Shumeiko N V , Osadcha K P 
Published: October 2024

Abstract
This paper aims to delineate the significant role of artificial intelligence (AI) in developing the soft skills of future information technology (IT) professionals. To achieve this goal, the authors give some relevant considerations concerning using AI in teaching humanitarian subjects and explore the use of AI to develop soft skills essential for future IT professionals. First, the analysis of the survey results confirms that students are not aware that using AI in education is very helpful. That is why many students (51.8%) do not use them at all. Therefore, we have an intention to give the examples of the use of AI tools for educational purposes to equip students, first of all, with understanding of soft skills value, and, secondly, let them complete the tasks that contribute to the development and improvement of their soft skills. Second, we defined that the soft skills (communication, negotiation, problem-solving, finding a common language with colleagues and clients, public speaking, and intercultural) are essential for conducting professional communication with colleagues in the sphere of IT to satisfy the demands of the IT industry in the modern-day world. Third, the paper contributes scientifically to investigating the integration of AI technologies in the university-level educational landscape. Integrating AI in higher education institutions offers a promising route for developing or boosting the soft skills necessary for future specialists in the field of IT. Drawing on a comprehensive review of literature focusing on the use of AI technologies in the contemporary world and considering the empirical data from the online survey, the study investigates the opportunities of AI tools to foster soft skills required to achieve the active collaboration of IT employees for companies.
2PM waveform from loop corrected soft theorems
Authors: Alessio Francesco , Di Vecchia Paolo 
Published: October 2024

AbstractWe introduce a classical version of the loop corrected soft graviton theorem and we use it to compute the universal part of the one-loop (2PM) waveform up to sub-subleading order in the energyωof the emitted graviton for spinless black holes scattering. In particular, we compute the action of the soft operators on the classically resummed four-point amplitude, that can be written in terms of the exponential of the eikonal phase (and is therefore non-perturbative in the Newton’s constantGN) and then we perform the usual Post-Minkowskian expansion in powers ofGN. We find perfect agreement with the existing 2PM literature at the ordersω−1,log⁡ωandωlog2⁡ω, which are universal. Furthermore, we use this method to compute the universal part of theωlog⁡ωcontribution to the 2PM waveform. Even if in the present analysis we limit ourselves to compute the soft 2PM waveform, our general formulae can be used to extract all universal PM orders of the terms connected with the infrared divergences up to non-linear memory contributions, once the impulse at the corresponding precision is known. Our approach, based on the resummed eikonal amplitude, gives a unified picture of the various computations of the classical soft graviton behaviour that are present in the literature since the seminal paper by Weinberg (1965Phys. Rev.140B516–24).