Reviewerly/exHarmony huggingface dataset:
Multilingual Interpreter and Translator

Abstract: Over the years, the issue of language differences has made it more difficult to communicate information effectively between countries and efficient information exchange has been hampered by the issue of linguistic differences. The conventional method employed to address linguistic barriers has not proven beneficial or effective. In the present, language interpreters need to be proficient in both the language they are translating into and the original language. The traditional methods of resolving language differences have not proven beneficial or productive. Additionally, language difference issues can make teaching foreign languages challenging. Multilingual Interpreters or Translators play a pivotal role in facilitating effective communication and understanding across languages. In order to simplify language learning and translation and promote stressfree communication, the study creates an Android language converter application, which can work more efficiently with an optimized code for the process of translation. Unlike traditional translation apps, our model leverages advanced natural language processing and machine learning algorithms to provide users with an intuitive and context-aware multilingual interpretation experience. In terms of communication, this application can help tourists integrate with the locals and obtain the necessary information.
Du Fu’s conspicuous negativity and Li Bai’s hidden positivity: a sentiment comparison and exploration

Abstract In the studies of classical Chinese poetry, the comparison between Li Bai and Du Fu is an everlasting topic, yielding many qualitative interpretations, among which a widely known but disputable one is Li’s positivity versus Du’s negativity. With the development of digital means, distant reading has become possible, and the sentiment issue can be further explored in quantitative ways. This research conducts a corpus-based sentiment comparison of Li and Du with a self-constructed sentiment dictionary. The Complete Collection of Tang Poems is used as a representative of Tang poets, and sentiment comparisons are made at the levels of poems, verses, and characters, as well as key characters extracted with the log-likelihood measure. Analyses show that (1) among Tang poets, Du is more negative at all of the above textual levels, while Li is only more positive at the key character level, proving the importance of key characters in readers’ perception of sentiment; (2) Li and Du both stand out among Tang poets with a negative depiction of the dark reality and a positive expression of grand ideals; and (3) Li’s positivity is largely embodied in his depictions of color, light, and temperature, while Du’s negativity is closely related to his psychological description. To conclude, this research has not only determined the sentiment difference between Li and Du but also located its sources in texts with a novel key character-based sentiment analysis approach. Keywords: Li Bai, Du Fu, sentiment analysis, corpus, keyness
Enhancing Color Scales for Active Data Physicalizations

Data Physicalization is the area that investigates how physical objects can encode data through their physical (geometry and materials) and visual features. In the information visualization field, color plays a fundamental role in communicating and encoding data, and it is no different for data physicalization. However, in the real world, color perception can be influenced by the material that composes the data physicalization, the environment’s brightness, and the characteristics of the light-emitting device. Thus, this article presents a process to evaluate and select colors to compose color palettes (categorical, sequential, and divergent) for data physicalizations, considering the perceptual distance between the chosen colors and the brightness of the light-emitting device for evaluating the influence of neighboring colors. Additionally, users perform Information Visualization tasks (identification, comparison, maximums, minimums, etc.) with different color palettes of the industry and literature in a physical 3D bar chart composed of LED strips. The initial results showed better performance by participants using the proposed color scales in Information Visualization tasks than those using traditional digital color scales. Finally, the steps carried out culminated in the proposition of a pipeline for evaluating and creating color scales for data physicalization, considering the features of the light-emitting device and the material used in the data physicalization.
TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.
MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification

Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss. Meta-neural networks, trained with meta-learning principles, generalize well across tasks, acquiring generalized weights. This enables the self-generation of tailored weighted functions for data biases. However, datasets may simultaneously exhibit imbalanced classes and corrupted labels, posing a challenge for current meta-models. To address this, this paper presents Meta-Loss Reweighting Network (MLRNet) with fusion attention features. MLRNet continually evolves sample loss values, integrating them with sample features from self-attention layers in a semantic space. This enhances discriminative power for biased samples. By employing minimal unbiased meta-data for guidance, mutual optimization between the classifier and the meta-model is conducted, endowing biased samples with more reasonable weights. Experiments on English and Chinese benchmark datasets including artificial and real-world biased data show MLRNet’s superior performance under biased data conditions.
Non-Local Means Hole Repair Algorithm Based on Adaptive Block

RGB-D cameras provide depth and color information and are widely used in 3D reconstruction and computer vision. In the majority of existing RGB-D cameras, a considerable portion of depth values is often lost due to severe occlusion or limited camera coverage, thereby adversely impacting the precise localization and three-dimensional reconstruction of objects. In this paper, to address the issue of poor-quality in-depth images captured by RGB-D cameras, a depth image hole repair algorithm based on non-local means is proposed first, leveraging the structural similarities between grayscale and depth images. Second, while considering the cumbersome parameter tuning associated with the non-local means hole repair method for determining the size of structural blocks for depth image hole repair, an intelligent block factor is introduced, which automatically determines the optimal search and repair block sizes for various hole sizes, resulting in the development of an adaptive block-based non-local means algorithm for repairing depth image holes. Furthermore, the proposed algorithm’s performance are evaluated using both the Middlebury stereo matching dataset and a self-constructed RGB-D dataset, with performance assessment being carried out by comparing the algorithm against other methods using five metrics: RMSE, SSIM, PSNR, DE, and ALME. Finally, experimental results unequivocally demonstrate the innovative resolution of the parameter tuning complexity inherent in-depth image hole repair, effectively filling the holes, suppressing noise within depth images, enhancing image quality, and achieving elevated precision and accuracy, as affirmed by the attained results.
The Developmentof Software to Automate the Laser Welding of a Liquefied Natural Gas Cargo Tank Using a Mobile Manipulator

The demand for liquefied natural gas (LNG) ships is increasing for various reasons. Despite their enormous size, cargo holds inside freighters transporting LNG have traditionally been constructed by welding to high-quality standards for safety. This process traditionally relies on manual labor or semi-automatic moving devices. In this study, a methodology was designed for robot-based automated laser welding inside large LNG cargo holds. The developed approach offers a practical solution to challenging issues such as the corrugation of the membrane that forms the inner walls of LNG cargo holds and the inter-floor movement of robots. This study analyzes and restructures the work for laser welding using mobile robots inside LNG cargo holds composed of membranes. For realistic constraints, such as inter-floor movement of robots and high-quality welding of membrane corrugations, methods integrated with manual work have been proposed. Additionally, for the overall membrane laser welding of the LNG cargo hold space, an automated method using robots was suggested. The developed methodology has been realized as operational software for the movement of robots for laser welding in LNG cargo holds.
The Quantum Amplitude Estimation Algorithms on Near-Term Devices: A Practical Guide

The Quantum Amplitude Estimation (QAE) algorithm is a major quantum algorithm designed to achieve a quadratic speed-up. Until fault-tolerant quantum computing is achieved, being competitive over classical Monte Carlo (MC) remains elusive. Alternative methods have been developed so as to require fewer resources while maintaining an advantageous theoretical scaling. We compared the standard QAE algorithm with two Noisy Intermediate-Scale Quantum (NISQ)-friendly versions of QAE on a numerical integration task, with the Monte Carlo technique of Metropolis–Hastings as a classical benchmark. The algorithms were evaluated in terms of the estimation error as a function of the number of samples, computational time, and length of the quantum circuits required by the solutions, respectively. The effectiveness of the two QAE alternatives was tested on an 11-qubit trapped-ion quantum computer in order to verify which solution can first provide a speed-up in the integral estimation problems. We concluded that an alternative approach is preferable with respect to employing the phase estimation routine. Indeed, the Maximum Likelihood estimation guaranteed the best trade-off between the length of the quantum circuits and the precision in the integral estimation, as well as greater resistance to noise.
Analyzing the Threats to Blockchain-Based Self-Sovereign Identities by Conducting a Literature Survey

Self-sovereign identity (SSI) is a digital identity management model managed in a decentralized manner. It allows identity owners to manage and store their digital identities in a software wallet, for example, on a smartphone, without relying on centralized providers. This approach tries to enhance the security and privacy of digital identities and, thereby, their owners. With the new eIDAS regulation, elements of SSI, such as the wallet, are being pushed onto the market. However, since the model is relatively new, the security threats are still not fully known. This is shown by a brief security analysis of selected existing SSI wallets. In order to get a picture of the known threats, we systematically analyze and categorize related work in the field of SSI and elements applied by SSI. We then evaluate their application to current SSI systems and identify future work.
RFTNet: Region–Attention Fusion Network Combined with Dual-Branch Vision Transformer for Multimodal Brain Tumor Image Segmentation

Brain tumor image segmentation plays a significant auxiliary role in clinical diagnosis. Recently, deep learning has been introduced into multimodal segmentation tasks, which construct various Convolutional Neural Network (CNN) structures to achieve excellent performance. However, most CNN-based segmentation methods have poor capability for global feature extraction. Transformer is good at modeling long-distance dependencies, but it can cause local information loss and usually has a high computational complexity. In addition, it is difficult to fully exploit the brain tumor features of different modalities. To address these issues, in this paper, we propose a region–attention fusion (RAF) network that combines a dual-branch vision Transformer (DVT), called RFTNet. In RFTNet, the DVT is exploited to capture the delicate local information and global semantics separately by two branches. Meanwhile, a novel RAF is employed to effectively fuse the images of the different modalities. Finally, we design a new hybrid loss function, called region-mixed loss function (RML) to calculate the importance of each pixel and solve the problem of class imbalance. The experiments on BrasTS2018 and BraTS2020 datasets show that our method obtains a higher segmentation accuracy than other models. Furthermore, ablation experiments prove the effectiveness of each key component in RFTNet.
Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.
An Asymmetric Feature Enhancement Network for Multiple Object Tracking of Unmanned Aerial Vehicle

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.
Research on Blockchain-Based Cereal and Oil Video Surveillance Abnormal Data Storage

Cereal and oil video surveillance data play a vital role in food traceability, which not only helps to ensure the quality and safety of food, but also helps to improve the efficiency and transparency of the supply chain. Traditional video surveillance systems mainly adopt a centralized storage mode, which is characterized by the deployment of multiple monitoring nodes and a large amount of data storage. It is difficult to guarantee the data security, and there is an urgent need for a solution that can achieve the safe and efficient storage of cereal and oil video surveillance data. This study proposes a blockchain-based abnormal data storage model for cereal and oil video surveillance. The model introduces a deep learning algorithm to process the cereal and oil video surveillance data, obtaining images with abnormal behavior from the monitoring data. The data are stored on a blockchain after hash operation, and InterPlanetary File System (IPFS) is used as a secondary database to store video data and alleviate the storage pressure on the blockchain. The experimental results show that the model achieves the safe and efficient storage of cereal and oil video surveillance data, providing strong support for the sustainable development of the cereal and oil industry.
Practical Exploration of Eco-Geological Survey Mapping in Qinghai–Tibet Plateau: Framework, Standard and Preliminary Cost Estimation

It is a common responsibility of all countries to protect the ecological environment and promote sustainable development. Eco-geology, which provides a basic guarantee for the rational utilization of natural resources, ecological protection and restoration, has gradually become a new hot spot of geological survey institutions in various countries. Eco-geological survey in the Qinghai–Tibet Plateau plays an important role in regional ecological protection and global change research. Setting up workable budgets symbolizes the competence of geological survey organizations in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary cost estimates. Hence, based on historical data and the practical experience of scientists who have been engaged in eco-geological survey in the Qinghai–Tibet Plateau for a long time, this research aimed to develop a cost estimation model using statistical learning methods for geological survey organizations to forecast preliminary costs during the early stages of an eco-geological survey project to fulfill different cost control and managerial functions. This research makes full use of modern remote sensing technology and reasonably arranges the input of ground survey, drilling, geophysical exploration and other workloads, supplemented by the existing ecological monitoring station data. It comprehensively sorts out the work means and workload of field and office work needed to complete the map work, and it puts forward a consistent index system and preliminary cost calculation framework. On this basis, based on the principle of matching quantity and price, a reasonable budget standard or market price is selected to calculate the budget estimate of a single map sheet, and then, through the regional adjustment coefficient, the budget estimate of a map sheet covering the whole Qinghai–Tibet Plateau is extended and calculated. The budget estimate will provide effective support for the declaration of eco-geological survey projects in the Qinghai–Tibet Plateau and the calculation of the scale of project funds so as to ensure the smooth development of eco-geological surveying in the region.
Military Decision-Making Process Enhanced by Image Detection

This study delves into the vital missions of the armed forces, encompassing the defense of territorial integrity, sovereignty, and support for civil institutions. Commanders grapple with crucial decisions, where accountability underscores the imperative for reliable field intelligence. Harnessing artificial intelligence, specifically, the YOLO version five detection algorithm, ensures a paradigm of efficiency and precision. The presentation of trained models, accompanied by pertinent hyperparameters and dataset specifics derived from public military insignia videos and photos, reveals a nuanced evaluation. Results scrutinized through precision, recall, map@0.5, mAP@0.95, and F1 score metrics, illuminate the supremacy of the model employing Stochastic Gradient Descent at 640 × 640 resolution: 0.966, 0.957, 0.979, 0.830, and 0.961. Conversely, the suboptimal performance of the model using the Adam optimizer registers metrics of 0.818, 0.762, 0.785, 0.430, and 0.789. These outcomes underscore the model’s potential for military object detection across diverse terrains, with future prospects considering the implementation on unmanned arial vehicles to amplify and deploy the model effectively.