Reviewerly/exHarmony huggingface dataset:
Identifying an Optimal Model for Blockchain Technology Adoption in the Agricultural Sector

Background: Blockchain adoption in agriculture is facing challenges. Some of its key challenges include lack of regulatory framework and unclear policies. Methods: This quantitative research based on a survey aims to examine the factors that influence blockchain implementation in the agricultural sector. Several theories including the technology acceptance model (TAM), the theory of planned behaviour (TPB), and the technology readiness index (TRI) were drawn upon to determine the factors influencing Blockchain adoption. The study was undertaken in Australia with 358 agricultural supply chain experts. Results: A range of novel findings were generated. While perceived usefulness, perceived ease of use, and attitude positively influenced Blockchain adoption, discomfort and insecurity were considered deterrents. Conclusions: The results can be used by relevant practitioners to improve the supply chain management for agriculture business. The findings can also inform a new direction for the research on the agricultural supply chain and the literature on logistics.
Universal Quantum Optimization with Cold Atoms in an Optical Cavity

Cold atoms in an optical cavity have been widely used for quantum simulations of many-body physics, where the quantum control capability has been advancing rapidly in recent years. Here, we show the atom cavity system is universal for quantum optimization with arbitrary connectivity. We consider a single-mode cavity and develop a Raman coupling scheme by which the engineered quantum Hamiltonian for atoms directly encodes number partition problems. The programmability is introduced by placing the atoms at different positions in the cavity with optical tweezers. The number partition problem solution is encoded in the ground state of atomic qubits coupled through a photonic cavity mode, which can be reached by adiabatic quantum computing. We construct an explicit mapping for the 3-SAT and vertex cover problems to be efficiently encoded by the cavity system, which costs linear overhead in the number of atomic qubits. The atom cavity encoding is further extended to quadratic unconstrained binary optimization problems. The encoding protocol is optimal in the cost of atom number scaling with the number of binary degrees of freedom of the computation problem. Our theory implies the atom cavity system is a promising quantum optimization platform searching for practical quantum advantage.
MegaDetectNet: A Fast Object Detection Framework for Ultra-High-Resolution Images

Addressing the challenge of efficiently detecting objects in ultra-high-resolution images during object detection tasks, this paper proposes a novel method called MegaDetectNet, which leverages foreground image for large-scale resolution image object detection. MegaDetectNet utilizes a foreground extraction network to generate a foreground image that highlights target regions, thus avoiding the computationally intensive process of dividing the image into multiple sub-images for detection, and significantly improving the efficiency of object detection. The foreground extraction network in MegaDetectNet is built upon the YOLOv5 model with modifications: the large object detection head and classifier are removed, and the PConv convolution is introduced to reconstruct the C3 module, thereby accelerating the convolution process and enhancing foreground extraction efficiency. Furthermore, a Res2Rep convolutional structure is developed to enlarge the receptive field and improve the accuracy of foreground extraction. Finally, a foreground image construction method is proposed, fusing and stitching foreground target regions into a unified foreground image. This approach replaces multiple divided sub-images with a single foreground image for detection, reducing overhead time. The proposed MegaDetectNet method’s effectiveness for detecting objects in ultra-high-resolution images is validated using the publicly available DOTA dataset. Experimental results demonstrate that MegaDetectNet achieves an average time reduction of 83.8% compared to the sub-image division method among various commonly used object detectors, with only a marginal 8.7% decrease in mAP (mean Average Precision). This validates the practicality and efficacy of the MegaDetectNet method for object detection in ultra-high-resolution images.
Video-Restoration-Net: Deep Generative Model with Non-Local Network for Inpainting and Super-Resolution Tasks

Although deep learning-based approaches for video processing have been extensively investigated, the lack of generality in network construction makes it challenging for practical applications, particularly in video restoration. As a result, this paper presents a universal video restoration model that can simultaneously tackle video inpainting and super-resolution tasks. The network, called Video-Restoration-Net (VRN), consists of four components: (1) an encoder to extract features from each frame, (2) a non-local network that recombines features from adjacent frames or different locations of a given frame, (3) a decoder to restore the coarse video from the output of a non-local block, and (4) a refinement network to refine the coarse video on the frame level. The framework is trained in a three-step pipeline to improve training stability for both tasks. Specifically, we first suggest an automated technique to generate full video datasets for super-resolution reconstruction and another complete-incomplete video dataset for inpainting, respectively. A VRN is then trained to inpaint the incomplete videos. Meanwhile, the full video datasets are adopted to train another VRN frame-wisely and validate it against authoritative datasets. We show quantitative comparisons with several baseline models, achieving 40.5042 dB/0.99473 on PSNR/SSIM in the inpainting task, while during the SR task we obtained 28.41 dB/0.7953 and 27.25/0.8152 on BSD100 and Urban100, respectively. The qualitative comparisons demonstrate that our proposed model is able to complete masked regions and implement super-resolution reconstruction in videos of high quality. Furthermore, the above results show that our method has greater versatility both in video inpainting and super-resolution tasks compared to recent models.
Dual-mimic learning for occluded pedestrian detection

Abstract It is still a great challenge to detect pedestrians in crowded scenes. Recent works mainly focus on enhancing the feature representation of the visible region. Although this strategy is effective, it fails to fully exploit the appearance information contained in the full-body region. To this end, we propose a Dual-Mimic learning model, which consists of the occluded to unoccluded (Occ-Unocc) and the full-body to visible (Full-Vis) mimic learning branches. Both branches can reduce the intra-class feature variances of pedestrians by different mimicking strategies. Besides, we define an occlusion attribute to describe the occlusion pattern of each detection. Based on the occlusion attribute, we propose the Occlusion-aware NMS (Occ-NMS), which can effectively remain the occluded instance with low confidence. We conduct extensive experiments on standard benchmarks of CityPersons and Caltech, and our method achieves a new state-of-the-art performance at 36.34% MR −2 and 25.07% MR −2 on heavy occlusion sets.
Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method

This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost.
Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case

Recently, there has been a growth in the number of studies addressing the automatic processing of low-resource languages. The lack of speech and text data significantly hinders the development of speech technologies for such languages. This paper introduces an automatic speech recognition system for Livvi-Karelian. Acoustic models based on artificial neural networks with time delays and hidden Markov models were trained using a limited speech dataset of 3.5 h. To augment the data, pitch and speech rate perturbation, SpecAugment, and their combinations were employed. Language models based on 3-grams and neural networks were trained using written texts and transcripts. The achieved word error rate metric of 22.80% is comparable to other low-resource languages. To the best of our knowledge, this is the first speech recognition system for Livvi-Karelian. The results obtained can be of a certain significance for development of automatic speech recognition systems not only for Livvi-Karelian, but also for other low-resource languages, including the fields of speech recognition and machine translation systems. Future work includes experiments with Karelian data using techniques such as transfer learning and DNN language models.
A Chinese Grammatical Error Correction Model Based On Grammatical Generalization And Parameter Sharing

Abstract Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few open-source annotated corpora for the CGEC task; the existing researches mainly concentrate on using data augmentation technology to alleviate the data-hungry problem. In this paper, rather than expanding training data, we propose a competitive CGEC model from a new insight for reducing model parameters. The model contains three main components: a sequence learning module, a grammatical generalization module and a parameter sharing module. Experimental results on two Chinese benchmarks demonstrate that the proposed model could achieve competitive performance over several baselines. Even if the parameter number of our model is reduced by 1/3, it could reach a comparable $F_{0.5}$ value of 30.75%. Furthermore, we utilize English datasets to evaluate the generalization and scalability of the proposed model. This could provide a new feasible research direction for CGEC research.
Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

INTRODUCTION: Machine translation is a modern natural language processing research field with important scientific and practical significance. In practice, the variation of languages, the limitation of semantic knowledge, and the lack of parallel language resources limit the development of machine translation.&#x0D; OBJECTIVES: This paper aims to avoid duplicating neural networks during the learning process and improve the ability to generalize complex neural network machine translation models with limited resources.&#x0D; METHODS: Textual material in the source language was studied, and a suitable textual material representation model was used to express complex, high-level, and abstract semantic information. Then, a more efficient neural network machine translation integration model was developed based on the control of written data and algorithms.&#x0D; RESULTS: Data mining must be applied to complex neural network machine translation systems based on transfer learning to standardize finite neural network models.&#x0D; CONCLUSION: Neural network-based embedded machine translation systems based on migration training require a small number of labelled samples to improve the system's permeability. However, this adaptive migration learning region approach can easily lead to over-learning problems in neural network machine translation models, thus avoiding excessive correspondences during the learning process and improving the generalization ability of the translation model with limited neural network resources.
Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning

Abstract Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions. Federated learning, a recent development in machine learning methods, has gained traction in privacy-sensitive tasks, such as personal voice assistants in home environments. However, its application in heterogeneous multi-domain scenarios for enhancing system customization remains underexplored. In this paper, we propose the utilization of federated learning in heterogeneous situations to enable adaptation across multiple domains. We also introduce a personalized federated learning algorithm designed to effectively leverage limited domain data, resulting in improved learning outcomes. Furthermore, we present a strategy for implementing the federated learning algorithm in practical, real-world continual learning scenarios, demonstrating promising results. The proposed federated learning method exhibits superior performance across a range of synthesized complex conditions and continual learning settings, compared to conventional training methods.
Reasoning with reasons: Lewis on common knowledge

Abstract David Lewis is widely regarded as the philosopher who introduced the concept of common knowledge. His account of common knowledge differs greatly from most later accounts in philosophy and economy, with the central notion of his theory being ‘having reason to believe’ rather than ‘knowledge’. Unfortunately, Lewis’s account is rather informal, and the argument has a few gaps. This paper assesses two major attempts to formalise Lewis’s account and argues that these formalisations are missing a crucial aspect of this account. Therefore, a new reconstruction is proposed, which explicitly discusses ‘reasons’ and uses a logic inspired by justification logic.
Decorative and applied art: the play of a creative person

The article is devoted to the analysis of the concept of the game and its presence in creative activity. The gameplay is presented as an important part of human life. The presence of both elements in a person's work shapes him as a "creative" and "creating" entity. The introduced characteristics are analyzed from the point of view of the historical context, where the concepts of a creative person and a person playing are equated to each other. Their similarity is established on the basis of the description of the signs of the game revealed at the beginning of the XX century. Game and art are considered abstractly from a specific cultural background, as functions that organize a person and preserve an abstract character. The features of the game highlighted in the article are given as evidence that plastic art stores it at its core as well as musical art. Their division within the cult and myth has led to the question of whether there is a game in art. The solution to this question lies in the field of the role of the participant in the game. Analysis from the point of view of the roles of the "creator" and "observer" allows us to uncover misconceptions about applied art and its game function.The findings showed that the position of the "observer" does not always allow you to see a complete picture of the process of plastic creativity. Due to the secrecy of most of the activity in the inner world of the artist, the observer cannot see the mechanical work of the gameplay. On this basis, he concludes about the insufficiency of plastic art. Analysis from the position of a creative person, on the contrary, indicates the full compliance of applied arts with the gameplay. On this basis, it is indicated that the game is not only an abstract concept that characterizes a person's life, but also the main component of any creative process.
Local-Aware Hierarchical Attention for Sequential Recommendation

Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have initially explored integrating self-attention into sequential recommendation, demonstrating promising results. However, existing methods have overlooked the intrinsic structure of sequences, failed to simultaneously consider the local fluctuation and global stability of users’ interests, and lacked user information. To address these limitations, we propose LHASRec (Local-Aware Hierarchical Attention for Sequential Recommendation), a model that divides a user’s historical interaction sequences into multiple sessions based on a certain time interval and computes the weight values for each session. Subsequently, the calculated weight values are combined with the user’s historical interaction sequences to obtain a weighted user interaction sequence. This approach can effectively reflect the local fluctuation of the user’s interest, capture the user’s particular preference, and at the same time, consider the user’s general preference to achieve global stability. Additionally, we employ Stochastic Shared Embeddings (SSE) as a regularization technique to mitigate the overfitting issue resulting from the incorporation of user information. We conduct extensive experiments, showing that our method outperforms other competitive baselines on sparse and dense datasets and different evaluation metrics.
Language learning strategies in EFL used by athlete students in sport language center

The aim of this research is to identify the type of English learning strategies as foreign language used by athlete students and to analyse on how to use language learning strategies in learning English as foreign language to their achievement in English speaking skill. This research was conducted on six athlete students in Sport Language Center of Surabaya who are able to speak English in International Competition. The data for this research were taken from observation and filling up questionnaire analysis. It can be concluded that language learning strategies in learning English as foreign language for the students were very useful and helpful. The learning strategies are believed to be responsible for successful language acquisition and significant in language learning. Thus, in learning English as a foreign language, appropriate language learning strategies were significant. Athlete students who are swimmers in Sport Language Center needed to know how to learn, and swimmer coach needed to know how to facilitate his process in teaching English. Hopefully this research can also inspire other researcher who are interested in conducting research on other aspects of language learning strategies.&#x0D; Keywords: Language Learning Strategy; Athlete; English as foreign language
An analysis of the implementation of the Indonesian culture in English class

As a multicultural country, Indonesia has many cultures that must be taught to its people, especially to university students through some university subjects. However, not all students understand well about their own culture, which becomes one of the main concerns of the lecturer and the students themselves. Therefore, the curriculum is then designed to enable students to learn about the culture aside from their courses. One of the lessons that also allows students to understand their own culture is English. In this study, the researcher chooses English II, the English lesson given to second-semester students. Students are encouraged to learn the language during the English class while also learning the Indonesian culture to know more about it and raise their awareness to preserve it. Some topics included Indonesian Literature, Wayang and Other Indonesian Art Performances, and some other issues that may be closely related to the Indonesian culture substances. The lesson problems were presented in some ways, including presentation, discussion, storytelling, and role-play. Therefore, this study aims to analyze how Indonesian culture is embedded in students’ lesson materials. This study uses observation and documentation as to its data collection. Meanwhile, the study revealed some classroom activities and assignments that indicate the implementation of the Indonesian culture in English II Classes. &#x0D; Keywords: Analysis, Implementation, Indonesian, Culture, English Class&#x0D;
EFL students’ speaking ability in descriptive text

This study aimed to find out EFL students’ speaking ability in a descriptive text. This research was a descriptive survey. The participants were chosen by using cluster random sampling technique where there were twenty students at the eleventh-grade students of SMA Negeri 4 OKU. The data were collected through oral test in the form of online test and telephone survey. WhatsApp application was used as an online platform in data collection. The result of the study showed that the total of students’ score was 1142 and mean of the students’ score was 57,1. There were 1 student (5%) who gains score 80-100 in very good criteria, 2 students (10%) who gain score 66-79 in good criteria, 10 students (50%) who gain score 56-65 in enough criteria, 5 students (25%) who gain score 46-55 in low criteria, and 2 students (10%) got score 45 in fail criteria. Based on the data, there were only 13 students (65%) who passed the students’ speaking ability. In conclusion, the frequency of criteria of students’ speaking ability is in enough criteria.&#x0D; Key words: EFL; Speaking abilit; Descriptive text
Breakthroughs, Challenges, and Implementation Path of Reading Teaching in the New Media Environment

Under the background of the information age, new media, as an important teaching tool in the field of education, has increasingly highlighted its unique and important teaching role. The development process of new media reveals its historical evolution from nothing to something, and its relatively stable transformation. The analysis and research of reading cognitive theory by many experts and scholars have provided a solid theoretical support for the integration of new media into reading teaching and its important function. Compared with traditional media, new media provides new teaching styles and choices for reading teaching with its advantages of flexible teaching in time and space, intuitive understanding and cognition of presented content, but the problems it may cause in teaching also bring challenges to reading teaching. Therefore, formulating and implementing scientific and effective countermeasures for reading teaching in the new media environment has become an important content of research and teaching.