Reviewerly/exHarmony huggingface dataset:
Development of English translation online teaching system and service function improvement based on image segmentation algorithm

With the continuous progress of Internet technology, so there is a higher demand for English translation. In this context, this paper realizes the design and improvement of the online English translation system by combining the image segmentation algorithm. After the experimental test of the algorithm of this system, if there is a lot of salt-and-pepper noise in the target image in the actual system application process, the method designed in this paper can reflect a better processing effect. Based on the analysis of performance parameters, this method can effectively process Gaussian noise images with high precision. After the completion of the system design, this paper tests the system. From the test and operation results, the software has reached the design objectives and completed the work tasks. The English translation online teaching system can form a safe, stable and high-bandwidth English translation teaching network and can realize the connection of the whole network. Users can share various software and hardware resources, completed the design of a translation platform that can realize rapid English communication, share experience and discuss, to effectively improve the learner’s initiative. In this paper, an effective online teaching system is designed by introducing image segmentation algorithm into the field of English translation.
Towards Enhanced Identification of Emotion from Resource-Constrained Language through a novel Multilingual BERT Approach

Emotion identification from text has recently gained attention due to its versatile ability to analyze human-machine interaction. This work focuses on detecting emotions from textual data. Languages, like English, Chinese, and German are widely used for text classification, however, limited research is done on resource-poor oriental languages. Roman Urdu (RU) is a resource-constrained language extensively used across Asia. This work focuses on predicting emotions from RU text. For this, a dataset is collected from different social media domains and based on Paul Ekman's theory it is annotated with six basic emotions, i.e., happy, surprise, angry, sad, fear, and disgusting. Dense word embedding representations of different languages is adopted that utilize existing pre-trained models. BERT is additionally pre-trained and fine-tuned for the classification task. The proposed approach is compared with baseline machine learning and deep learning algorithms. Additionally, a comparison of the current work is also performed with different approaches for the same task. Based on the empirical evaluation, the proposed approach performs better than the existing state-of-the-art with an average accuracy of 91%.
Zero-Watermarking for Vector Maps Combining Spatial and Frequency Domain Based on Constrained Delaunay Triangulation Network and Discrete Fourier Transform

With its lossless properties, zero-watermarking has attracted a lot of attention in the field of copyright protection for vector maps. However, the common zero-watermarking algorithm puts too much emphasis on mining for global features, making it vulnerable to cropping attacks, and the robustness is not comprehensive enough. This study provides a vector map zero-watermarking scheme that utilizes spatial statistical information and frequency domain transformation methods in an effort to solve the aforementioned issue. In order to make the scheme more resistant to cropping and compression, it is constructed on the basis of feature point extraction and point constraint blocking of the original vector map. Within each sub-block, feature points are used to build constraint Delaunay triangulation networks (CDTN), and the angular values within the triangle networks are then extracted as spatial statistics. The angle value sequence is further transformed by discrete Fourier transform (DFT), and the binarized phase sequence is used as the final feature information to build a zero watermark by executing an exclusive disjunction operation with the encrypted copyright watermark image, both of which contribute to the scheme's robustness and security. The results of the attack experiments show that the proposed vector map zero-watermarking can restore identifiable copyright images under common geometric attacks, cropping attacks, and coordinate system transformations, demonstrating a high level of robustness. The theoretical basis for the robustness of this watermarking scheme is the stability of CDTN and the geometric invariance of DFT coefficients, and both theory and experiment validate the method's validity.
Multispectral Remote Sensing Monitoring of Soil Particle-Size Distribution in Arid and Semi-Arid Mining Areas in the Middle and Upper Reaches of the Yellow River Basin: A Case Study of Wuhai City, Inner Mongolia Autonomous Region

Particle size distribution is an important characteristic of reclaimed soil in arid and semi-arid mining areas in western China, which is important in the ecological environment protection and control of the Yellow River Basin. Large-scale coal resource mining disturbances have caused serious damage to the fragile ecological environment. The timely and accurate dynamic monitoring of mining area topsoil information has practical significance for ecological restoration and management evaluation. Investigating Wuhai City in the Inner Mongolia Autonomous Region of China, this study uses Landsat8 OLI multispectral images and measured soil sample particle size data to analyze soil spectral characteristics and establish a particle size content prediction model to retrieve the particle size distribution in the study area. The experimental results and analysis demonstrate that: (1) the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version) atmospheric correction model is more accurate than the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) model in arid and semi-arid areas with undulating terrain; (2) 0–40 cm is the optimum soil thickness for modeling and predicting particle size content in this study; and (3) the multi-band prediction model is more precise than the single-band prediction model. The multi-band model’s sequence of advantages and disadvantages is SVM (Support Vector Machine) &gt; MLR (Multiple Linear Regression) &gt; PLSR (Partial Least Squares Regression). Among them, the 6SV-SVM model has the highest precision, and the prediction precision R2 of the 3 particle sizes’ contents is above 0.95, which can effectively predict the soil particle-size distribution and provide effective data to support topsoil quality change monitoring in the mine land reclamation area.
Data Defense: Examining Fintech’s Security and Privacy Strategies

This article aims to investigate the problem of security and privacy in the fintech business, since the use of digital technologies has increased in last few years. In this context, we conducted a survey of fintech companies to understand how they are addressing the security and privacy issue and what are the potential risks and benefits for consumers. According to the results, fintech companies are exploiting a range of technical, regulatory, and compliance measures to ensure the security and privacy of financial data, such as encryption, access controls, and data governance policies. However, we attempted to pinpoint open issues as well as prospective research directions. In such settings, there is a need to better understand the efficiency of different privacy and security measures, as well as the potential risks and advantages for customers while they are using fintech services. This article recommends that future research broaden its scope and delve further into the security and privacy issues of specific types of fintech services.
Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

In order to effectively improve the path‐finding capability of a multirobot system in a decentralized control approach, a dynamic motion planning model based on graph neural networks and historical information (GNNHIM) is proposed. Due to the limited sensing range of the robot's onboard sensors, GNNHIM uses convolutional neural networks to extract features from the heterogeneous environmental information collected and analyzes the motion trends of other robots in conjunction with the historical path information stored locally. After finishing feature fusion, each robot exchanges its feature vectors with other robots still within sensing range to obtain the next action by the graph neural network model processing. Herein, imitation learning is used to train robots to choose behavioral strategies to maximize team benefit. The experimental results show that GNNHIM can make path planning for multirobot systems more efficient and reliable, enabling all robots to reach their own goal within a limited time in 95.1% of the experimental dataset. Moreover, GNNHIM still has great generalization when applied to unseen scenarios and larger scale robot systems. GNNHIM in unseen scenarios has improved the success rate by 6%‐34% and reduced the detour percentage by 3.8% on average compared to the other scheduling model.
A study of quadratic Diophantine fuzzy sets with structural properties and their application in face mask detection during COVID-19

&lt;abstract&gt;&lt;p&gt;During the COVID-19 pandemic, identifying face masks with artificial intelligence was a crucial challenge for decision support systems. To address this challenge, we propose a quadratic Diophantine fuzzy decision-making model to rank artificial intelligence techniques for detecting masks, aiming to prevent the global spread of the disease. Our paper introduces the innovative concept of quadratic Diophantine fuzzy sets (QDFSs), which are advanced tools for modeling the uncertainty inherent in a given phenomenon. We investigate the structural properties of QDFSs and demonstrate that they generalize various fuzzy sets. In addition, we introduce essential algebraic operations, set-theoretical operations, and aggregation operators. Finally, we present a numerical case study that applies our proposed algorithms to select a unique face mask detection method and evaluate the effectiveness of our techniques. Our findings demonstrate the viability of our mask identification methodology during the COVID-19 outbreak.&lt;/p&gt;&lt;/abstract&gt;
Classical simulation of variational quantum classifiers using tensor rings

In recent times, Variational Quantum Circuits (VQC) have been widely adopted to different tasks in machine learning such as Combinatorial Optimization and Supervised Learning. With the growing interest, it is pertinent to study the boundaries of the classical simulation of VQCs to effectively benchmark the algorithms. Classically simulating VQCs can also provide the quantum algorithms with a better initialization reducing the amount of quantum resources needed to train the algorithm. Even though Matrix Product State representations have been extensively used for quantum state approximation, their capacity is limited in simulating quantum circuits due to the exponential complexity in circuit depth. This manuscript proposes an algorithm that compresses the quantum state within a circuit using a noisy tensor ring representation which allows for the implementation of VQC based algorithms on a classical simulator at a fraction of the usual storage and computational complexity. Using the tensor ring approximation of the input quantum state, we propose a method that applies the parametrized unitary operations while retaining the low-rank structure of the tensor ring corresponding to the transformed quantum state, providing an exponential improvement of storage and computational time in the number of qubits and layers. This approximation is used to implement the tensor ring VQC (TRVQC) for the task of supervised learning on Iris and MNIST datasets to demonstrate the performance of the proposed method compared with the implementations from classical simulator using Matrix Product States (MPS). TRVQC has a test accuracy of 82.63% compared to the benchmark of 83.68% on Iris dataset whereas the former outperforms the latter on a reduced MNIST dataset with TRVQC having an accuracy of 83.73% compared to the benchmark 81.02%, showcasing the comparable performance of the proposed algorithm with the MPS framework.
Structural redundancy reduction based efficient training for lightweight person re-identification

Deploying person re-identification (Re-ID) models in edge devices is of great importance in the field of intelligent surveillance. It can not only relieve the computational pressure on cloud data centers, but also effectively protects data privacy (e.g., pedestrian identity). However, current Re-ID methods mostly rely on the large-scale backbone with huge parameters, making them impossible to deploy in edge devices. To solve this problem, light-weight Re-ID backbones came into being. The current proposed light-weight Re-ID backbones are all multi-branch parallel structures based on multi-scale ideas. Nonetheless, an excessive number of parallel branches tend to extract redundant information, which takes up too much storage resources and easily leads to overfitting of the model structure. These problems limit the performance of the current models. In this paper, we innovatively propose a blocks-by-blocks combine-and-expand (BB-CE) training method. First we construct a base architecture using the proposed single-branch multi-scale (SMS) module. Then, by combining and expanding the blocks in the base architecture stage by stage during training, BB-CE training method can reduce the redundancy of the structure, utilize the multi-scale information more efficiently, and thus improve the matching accuracy of re-identification. Extensive experimental results on four generic person Re-ID datasets demonstrate that the proposed method outperforms the state-of-the-art algorithms.
Self-Adaptive Hybridized Lion Optimization Algorithm with Transfer Learning for Ancient Tamil Character Recognition in Stone Inscriptions

Tamil character recognition is an important field of research in pattern recognition and it is a technical challenge than other languages due to similarity and complexity of characters. Stone inscriptions reveal the details of lavishness, lifestyle, economic conditions, culture, and also of the managerial regulations followed by various rulers and dynasties particular to those regions. However, due to the long history of ancient stone inscription, natural erosion and lack of early protection measures, there are lot of noise in the existing ancient stone inscriptions, which create adverse effects on reading these stone inscriptions and their aesthetic appreciation. The research challenge in recognizing Tamil characters is mainly because of the characters with a number of holes, loops and curves. The number of letters in Tamil language is higher when compared to other languages. Even though there are various approaches provided by the researchers, challenges and issues still prevail in recognition of tamil text in stone inscriptions. In the existing systems, detection algorithms fail to produce desired accuracy and hence stone inscription recognition using transfer learning, a promising method is proposed in this paper. Lion Optimization Algorithm (LOA) is applied to optimize brightness and contrast and then stone inscription images are pre-processed for noise removal and then each character is separated by identifying contours. Characters are recognized using Transfer Learning (TL), a Deep Convolution Neural Network-based multi classification approach. The proposed hybrid model Self-Adaptive Lion Optimization Algorithm with Transfer Learning (SLOA-TL) when implemented in images of stone inscriptions achieves better accuracy and speed than other existing methods. It serves as an efficient design for recognition of tamil characters in stone inscriptions and preserving tamil traditional knowledge.
DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation

Automatic segmentation of liver tumors is crucial to assist radiologists in clinical diagnosis. While various deep learningbased algorithms have been proposed, such as U-Net and its variants, the inability to explicitly model long-range dependencies in CNN limits the extraction of complex tumor features. Some researchers have applied Transformer-based 3D networks to analyze medical images. However, the previous methods focus on modeling the local information (eg. edge) or global information (eg. morphology) with fixed network weights. To learn and extract complex tumor features of varied tumor size, location, and morphology for more accurate segmentation, we propose a Dynamic Hierarchical Transformer Network, named DHT-Net. The DHT-Net mainly contains a Dynamic Hierarchical Transformer (DHTrans) structure and an Edge Aggregation Block (EAB). The DHTrans first automatically senses the tumor location by Dynamic Adaptive Convolution, which employs hierarchical operations with the different receptive field sizes to learn the features of various tumors, thus enhancing the semantic representation ability of tumor features. Then, to adequately capture the irregular morphological features in the tumor region, DHTrans aggregates global and local texture information in a complementary manner. In addition, we introduce the EAB to extract detailed edge features in the shallow fine-grained details of the network, which provides sharp boundaries of liver and tumor regions. We evaluate DHT-Net on two challenging public datasets, LiTS and 3DIRCADb. The proposed method has shown superior liver and tumor segmentation performance compared to several state-of-the-art 2D, 3D, and 2.5D hybrid models.
Relaxed least square regression with ℓ2,1-norm for pattern classification

This work aims to address two issues that often exist in least square regression (LSR) models for classification tasks, which are (1) learning a compact projection matrix for feature selection and (2) adopting relaxed regression targets. To this end, we first propose a sparse regularized LSR framework for feature selection by introducing the [Formula: see text] regularizer. Second, we utilize two different strategies to relax the strict regression targets based on the sparse framework. One way is to exploit the [Formula: see text]-dragging technique. Another strategy is to directly learn the labels from the inputs and constrain the distance between true and false classes simultaneously. Hence, more feasible regression schemes are constructed, and the models will be more flexible. Further, efficient iterative methods are derived to optimize the proposed models. Various experiments on image databases intend to manifest our proposed models have outstanding recognition capability compared with many state-of-the-art classifiers.
An overlapping oriented imbalanced ensemble learning algorithm with weighted projection clustering grouping and consistent fuzzy sample transformation

Class imbalance and class overlapping problems can exist simultaneously in the imbalanced learning. However, most of the existing algorithms mainly focus on the former. Although some recent algorithms focus on the class overlapping problem, they do not effectively identify the overlapping region, resulting in a loss of sample information, and they are always applied to the original samples with low quality. To address these problems, this paper proposes an imbalanced ensemble learning algorithm based on weighted projection clustering grouping and consistent fuzzy sample transformation (PCGDST-IE). Firstly, a weighted projection clustering combination framework (WPCC) guided by Davies-Bouldin clustering effectiveness index (DBI) is designed to obtain high-quality clusters and the clusters are combined to form cross-complete subsets (CCS) with low overlapping. Secondly, a stage-wise hybrid sampling algorithm is designed to realize the de-overlapping and balancing of subsets. Finally, a local–global structure consistency mechanism (LGSCM) is constructed by fuzzy clustering and domain adaption, thereby reducing class overlapping and improving the quality of samples in subsets. Weak classifiers are trained on the balanced subsets, and fused. More than 30 public datasets and over ten representative algorithms are chosen to verify the proposed method. The experimental results show that the PCGDST-IE is significantly better in terms of anti-overlapping, Recall, F1-M, G-M, AUC, and diversity. The major originality of the paper is: (a) proposing the WPCC to realize weighted projection clustering for subsets generation; (b) proposing the SHS to balance class imbalance and overlapping better;(c) proposing the LGSCM for sample transformation to address the quality of subsets; and (d) forming an imbalanced algorithm to better solve the class imbalance and class overlapping problems simultaneously.
NFT Marketplace using Blockchain

Blockchain is a new, emerging, and pioneering technology under which the society has evolved from the existing Web 2.0 to the innovative Web 3.0. Blockchain paves the way to a decentralized system diverging from the current centralized system. One such captivating phenomenon in the Blockchain would be that of NFTs. NFTs are non-fungible tokens; they are a form of digital art and could be anything from pictures, music, videos, or games. NFTs could be considered the real-world equivalent of indispensable assets like gold, silver, stocks, or bonds. They are unique and have predominant characteristics that make them invaluable to their collectors, admirers or even creators allowing them to be a great source of investment and trading. This great potential in acquiring a large capital is what makes the establishment of a platform that could be used by both creators and consumers for doing dealings in NFTs exceedingly compelling. This paper puts forth a way to develop an NFT marketplace i.e., a decentralized application(dApps) for the trade of NFTs. For this cutting-edge application, some key and fundamental concepts such as smart contracts, wallets, token standards and a lot more are explored. A system design has been proposed that highlights all the essential features like minting, listing, buying, and selling of NFTs. A precise analysis of the benefits and limitations associated with NFTs, and their marketplaces is done. Benefits being immutability, security and tokenization and the limitations being gas fees, legal issues, and environmental concerns, to properly determine the feasibility and value of developing such an application.
Decentralized E-health Patient Record Management System using Blockchain and IPFS

The usage of Electronic Health Records (EHR) is becoming increasingly widespread globally. However, there are several issues with the current EHR systems, particularly in terms of privacy and security. Some of the major challenges faced by the current systems are limited infrastructure, shortage of trained healthcare personnel, lack of funding, limited usability, data privacy, lack of proper planning and limited access to technology. The proposed solution addresses many of these problems by utilizing a public Ethereum blockchain to connect hospitals, patients, and doctors worldwide. By utilizing RSA asymmetric key cryptography, the system ensures secure storage and controlled access to records. Patients are given full control over their records and are able to grant or revoke access for hospitals or healthcare centers. Additionally, the system employs IPFS (Interplanetary File System) for record storage to offer the benefits of being decentralized and providing immutability of records. Furthermore, our model is able to maintain disease statistics while maintaining patient privacy.