Reviewerly/exHarmony huggingface dataset:
MiLMo:Minority Multilingual Pre-Trained Language Model

Pre-trained language models are trained on large-scale unsupervised data, and they can fine-tune the model only on small-scale labeled datasets, and achieve good results. Multilingual pre-trained language models can be trained on multiple languages, and the model can understand multiple languages at the same time. At present, the search on pre-trained models mainly focuses on rich resources, while there is relatively little research on low-resource languages such as minority languages, and the public multilingual pre-trained language model can not work well for minority languages. Therefore, this paper constructs a multilingual pre-trained model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. To solve the problem of scarcity of datasets on minority languages and verify the effectiveness of the MiLMo model, this paper constructs a minority multilingual text classification dataset named MiTC, and trains a word2vec model for each language. By comparing the word2vec model and the pre-trained model in the text classification task, this paper provides an optimal scheme for the downstream task research of minority languages. The final experimental results show that the performance of the pre-trained model is better than the word2vec model, and it has achieved the best results in minority multilingual text classification. The multilingual pre-trained model MiLMo, multilingual word2vec model and multilingual text classification dataset MiTC are published on https://milmo.cmli-nlp.com/.
Learning-Based Scalable Video Coding with Spatial and Temporal Prediction

In this work, we propose a hybrid learning-based method for layered spatial scalability. Our framework consists of a base layer (BL), which encodes a spatially downsampled representation of the input video using Versatile Video Coding (VVC), and a learning-based enhancement layer (EL), which conditionally encodes the original video signal. The EL is conditioned by two fused prediction signals: a spatial inter-layer prediction signal, that is generated by spatially upsampling the output of the BL using super-resolution, and a temporal inter-frame prediction signal, that is generated by decoder-side motion compensation without signaling any motion vectors. We show that our method outperforms LCEVC and has comparable performance to full-resolution VVC for high-resolution content, while still offering scalability.
Learned Image Compression with Enhanced Dynamic Spatial Aggregation and Asymmetric Entropy model

Learned image compression (LIC) has shown significant potential and better rate-distortion performance than traditional techniques. However, existing CNN-based approaches or window-based self-attention methods can only capture spatial information within fixed ranges. To tackle this limitation, we propose a novel method with dynamic spatial aggregation for transform coding. Our approach introduces enhanced adaptive aggregation, which generates kernel offsets to capture relevant information within content-dependent ranges, improving the transform process. Furthermore, we define a generalized coarse-to-fine entropy model that considers global context, channel-wise information, and spatial context in a coarse-to-fine manner. Additionally, our method takes into full consideration the model’s efficiency and complexity. By introducing the asymmetric entropy model structure and efficient heterogeneous convolution, our approach maintains lower coding complexity and higher decoding speed while ensuring performance. Experimental results demonstrate a substantial improvement in rate-distortion performance achieved by our method when compared to traditional compression methods like VTM and BPG, as well as some LIC methods.
Self-Supervised Learning Based on Similar Users for Sequential Recommendation

Sequential Recommendation (SR) predicts the next interaction behavior via modeling the interaction between the user and the item over a time sequence. A series of works applied Self-Supervised Learning (SSL) in SR to obtain better user representations. Although these efforts proved effective, they only focused on the information of the user itself and ignores self-supervised signals from other users. Due to the widely observed homogeneity in recommender systems, these signals from other users are also vital for user representation. To this end, we propose a novel framework, Self-Supervised Learning based on Similar users for Sequential Recommendation (SSLSRec). We present a contrastive learning objective in SSLSRec to consider augmented views from the same user and similar users as positive samples. Moreover, we propose novel Insert and Substitute augmentation methods to construct more reasonable augmentation views for user sequences. Extensive experiments demonstrate the effectiveness of SSLSRec.
A Graph Convolutional Network with Ensemble Classifiers for 2D Skeleton-based Action Recognition

In this paper, we address the problem of action recognition from 2D skeletons produced by pose estimation algorithms. In practical applications, inaccurate joint information is inevitable due to self-occlusion and interaction with objects, which pose great challenges to 2D skeleton-based action recognition. To this end, we propose a graph convolutional network with ensemble classifiers to improve the robustness against noisy skeleton data. A global classifier learned from representations of the entire skeleton can capture the overall movement of the human body, while a set of part-based classifiers focuses on crucial local motion patterns. To further explore the correlations among joints within a part, we apply a self-attention mechanism to obtain context-aware part representations. Considering the existence of noisy joints, we develop a confidence-based weighting strategy to suppress classifiers based on inaccurate part representations. Experiments on the large-scale NTU-RGB+D dataset verify that the ensemble classifiers can boost the action recognition performance, especially on low-quality skeleton data with dense noise.
A Variational Bayesian Maximum Correntropy Cubature Kalman Filter with Adaptive Kernel Bandwidth

This paper focuses on the estimation problem in the presence of non-Gaussian measurement noise encountered in dynamic systems. The proposed solution is based on a variational Bayesian maximum correntropy cubature Kalman filter with adaptive kernel bandwidth that uses the Mahalanobis distance to adapt in real time the kernel bandwidth. The proposed filter is compared to some recent cubature Kalman filtering approaches, using the univariate nonstationary growth model benchmark. The obtained results demonstrate that the proposed method leads to the estimated values less affected by non-Gaussian measurement noises than other recent cubature Kalman-based filter.
Distributed Neighbor Selection on Matrix-weighted Networks

This paper explores distributed neighbor selection in matrix-weighted networks, where a matrix-valued weight captures the interdependencies among agents’ states. We demonstrate the feasibility of preserving and, in certain cases, improving the convergence rate of matrix-weighted networks by having agents exclusively engage with a subset of their neighbors. Leveraging the inherent monotonicity of Laplacian matrix eigenvectors, we introduce a novel neighbor selection criterion that reduces network connections while enhancing convergence rates. Existing neighbor selection strategies often require acquiring information from all nodes before making decisions. In order to propose a distributed neighbor selection strategy for matrix-weighted networks, we establish a numerical relationship between Laplacian eigenvectors and a locally measurable metric. In essence, this paper extends prior findings from scalar-weighted networks to matrix-weighted networks.
Cross-lingual Related Events Recognition Methods Based on The Event Central News Sets

To help the public fully understand the ins and outs of an international or regional major event, we propose two cross-lingual related events recognition methods to acquire more related events in the news on different countries’ internet. Firstly news in different languages that focus on a same event are clustered into a news set. Then two method will be used together to determine whether two events are related. One is explicit related event recognition method which determines that by checking whether there is a description of one event can be found from another event central news set. The other one is implicit related event recognition method which determines that by a SVM classifier with several correlation and similarity features of two events. The experimental results show that the two methods are complementary to each other, and it is practical to use them together.
Multi - Environment Image Dehazing Algorithm Based on Color Channel Attenuation Difference

In this paper, we propose a multi-environment image dehazing algorithm based on the color channel attenuation difference(ME-DCAD). The algorithm possesses several unique features: firstly, it uses a combination of the Beer-Lambert law and the light square attenuation law to more accurately define the relationship between light attenuation and distance; secondly, it estimates the transmission rate by calculating the difference in attenuation with distance through different color channels, which results in good dehazing effects in both underwater and hazy environments; thirdly, it considers the problem of dust accumulation on the lens and can be applied in environments including scenarios involving coke inspection in factories. Through comparative experiments with similar methods, our proposed method exhibits certain image deblurring and enhancement performance and performs well in various environments, improving the image quality obtained in hazy environments.
A Blockchain-based for Trustworthy Tourism Service Level Agreement

The tourism industry is a new sector that stimulates consumer spending and drives economic growth, with the internet providing effective measures for its rapid development. Traditional online tourism service platforms, managed by a single entity, face trust barriers between different industries, hindering further development in tourism. The open and transparent nature of blockchain can effectively solve trust issues. However, existing solutions lack a study on the business processes of tourism services. The tourism industry still facing challenges such as cumbersome business processes, low coordination efficiency among stakeholders, and difficulties in service execution monitoring. Based on the above issues, this paper proposes a blockchain-based tourism service level agreement execution framework to establish a tourism service level agreement (TSLA) between tourists and service providers to realize the service provider's commitment to tourists. Furthermore, this paper designs a Tourism Service Level Agreement Smart Contract (TSLA-SC) based on oracle technology, stores the service execution process on the blockchain, tracks potential malicious behaviors of service providers, and sets corresponding reward and punishment measures to automate the TSLA execution process. Finally, experiments are conducted to test the cost and time expenses of TSLA-SC, with results indicating that the proposed solution can operate at lower costs, demonstrating good usability in terms of cost overhead.
Dual-Cell Recurrent Network for Target-Oriented Opinion Word Extraction on Global Fields

Target-oriented opinion word extraction (TOWE) is critical in aspect-based sentiment analysis. It aims at extracting opinion words that are related to aspect terms. Existing TOWE approaches primarily focused on explicit or implicit target aspects. However, few methods dealt with them simultaneously. For compensating this limitation, this study proposes a dual-cell recurrent network (DCRN) that combines aspect term extraction (ATE) and target-oriented opinion word extraction. The DCRN model is trained and evaluated on global fields, including explicit and implicit target aspects. Empirical results demonstrate that the proposed DCRN model outperforms existing methods by an average of 4.90% on the SemEva114–16 datasets. Furthermore, the DCRN model achieves higher Macro-F1 values than the IOG model on the Restaurant 14–16 datasets by 8.97%, 7.90 %, and 8.70 %, respectively. These results indicate that the DCRN model significantly improves the performance of TOWE and exhibits robust generalization capabilities.
Tackling the Duality of Obstacles and Targets in Shared Control Systems: A Smart Wheelchair Table-Docking Example

Many studies have shown that a smart wheelchair could improve the quality of life of people with restricted mobility by providing them with more freedom in the daily activities they can undertake independently. In addition to enhancing independent mobility, it is important to ensure safety for wheelchair users and those around them. To date, previous studies have mostly focused on (semi-)autonomous navigation or obstacle avoidance. By contrast, in this study, we tackle the challenging, but important problem of safely docking to tables. We propose a robotic navigation assistance, applied to electric powered wheelchairs using Time-of-Flight (ToF) sensors to facilitate table-docking for users. To meet this objective, we designed a low-cost sensor system that was integrated into our smart wheelchair prototype, which can detect a table and accurately estimate its height. We then developed a robust algorithm to deliver the manoeuvring assistance. First, we simulated the smart wheelchair system within Unity3D to find the best positions for the ToF sensors and evaluate the accuracy of the docking system, employing different table styles. Then, we experimentally validate the system on our physical wheelchair, using varying angles of approach, which demonstrate its feasibility.
Combining Self-Organizing Map with Reinforcement Learning for Multivariate Time Series Anomaly Detection

Anomaly detection plays a critical role in condition monitors to support the trustworthiness of Cyber- Physical Systems (CPS). Detecting multivariate anomalous data in such systems is challenging due to the lack of a complete comprehension of anomalous behaviors and features. This paper proposes a framework to address time series multivariate anomaly detection problems by combining the Self-Organizing Map (SOM) with Deep Reinforcement Learning (DRL). By clustering the multivariate data, SOM creates an environment to enable the DRL agents interacting with the collected system operational data in terms of a tabular dataset. In this environment, Markov chains reveal the likely anomalous features to support the DRL agent exploring and exploiting the state-action space to maximize anomaly detection performance. We use a time series dataset, Skoltech Anomaly Benchmark (SKAB), to evaluate our framework. Compared with the best results by some currently applied methods, our framework improves the F1 score by 9% from 0.67 to 0.73.
Hybrid Intelligent-Annotation Organ Segmentation on Medical Datasets

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency.
A Scalable Hierarchical-Domain Blockchain-Based Sharding System Towards Collaborative Sensing in Power Trading

Benefiting from the popularization of 6G and loT, blockchain-based collaborative sensing of power trading has received extensive attention from academia and industry. However, the low scalability seriously hinders the further widespread application of blockchain technology. Therefore, this paper proposes a blockchain-based hierarchical-domain sharding system BCShard, which adopts the client-edge-cloud three-layer architecture and adopts an improved privacy protection clustering algorithm based on historical transactions to securely implement blockchain sharding and efficiently process cross-shard transactions. In addition, we implement a blockchain sharding system prototype system based on open-source codes. Detailed theoretical analysis and experimental results show that our system is compared with other state-of-the-art solutions in different metrics, demonstrating the scalability, efficiency and security of our system in the PloT scenario.
Serendipity-Oriented Recommender System with Dynamic Unexpectedness Prediction

With unexpectedness as a component of serendipity, many previous studies on serendipity-oriented recommender systems have quantified the degree of unexpectedness of items for users as a score. A user's browsing and rating history is necessary for the score calculation. These studies either treated all histories as equal or used only the most recent histories. However, these calculation methods cannot cope with unexpectedness in the case of constant change owing to fluctuations in user preferences and public popularity. In this study, we propose a serendipity-oriented recommender system that sequentially calculates the unexpectedness score and predicts the score in the recommendation time. The proposed system consists of a reranking algorithm that reranks a recommendation list generated by accuracy-oriented recommender systems. It also introduces a parameter to adjust users' ability to accept unexpectedness and aims for serendipitous recommendations that are in line with user preferences. The experiment on two benchmark datasets showed two results: the first is the proposed system improved the serendipitous metric by 0.061 points compared with accuracy-oriented systems before reranking and by 0.045 points compared with them without using the acceptance parameter. The second is the error between the predicted and ground truth of the unexpectedness score was smaller for a large dataset.
Pengaruh Metode Pembelajaran Problem Solving Terhadap Kemampuan Membaca Kritis Siswa Kelas VIII SMPN 4 Marioriawa

This study aims to describe the effect of problem solving learning method on critical reading ability of grade VIII students of SMPN 4 Marioriawa. This research is a quantitative research with one group pretest-posttest design. This research involved VIII grade students of SMPN 4 Marioriawa totalling 35 people. The data collection technique in this study used a multiple choice test totalling 20 questions. The results of data analysis showed that: (1) the critical reading ability of grade VIII students of SMPN 4 Marioriawa before the application of problem solving learning method was categorised as low with an average score of 10.37; (2) the critical reading ability of grade VIII students of SMPN 4 Marioriawa after the application of problem solving learning method was categorised as high with an average score of 16.26; and (3) the results of hypothesis testing showed that the sig. (2-tailed) value of 0.000 &lt;0.05, so it is concluded that there is an effect of problem solving learning method on critical reading ability of grade VIII students of SMPN 4 Marioriawa.
Chaotic Phenomenal and One-dimensional Logistic Map

Introducing the basic properties of logistic map. The paper includes the proofing of the stability of fixed points in different domain of and determining the property of period doubling of Logistic map, describing Chaos phenomenon in logistic map by showing the stability of fixed points in different intervals of , researching 3-period limit cycle in the Logistic Map bifurcation diagram, showing a special characteristic of the logistic map on the region on the bifurcation diagram that exist 3-period limit cycles, collecting the different value of at one, two, four, eight, sixteen and thirty-two pitchfork points from the Logistic Map Bifurcation diagram to calculate Feigenbaum constant and proving the Feigenbaum can also be used to determine the pitchfork bifurcation speed of logistic map when r is between zero to negative two. Showing the basic properties of the Mandelbrot Set. Showing the result of computer simulation in real part to the Mandelbrot Set to determine the relationship between the Logistic Map bifurcation diagram and Mandelbrot Set.
Research on Image Up-scaling and Super-resolution Based on Convolutional Neural Network

Image upsampling and super-resolution techniques are of great significance in many fields, which can improve image quality and enhance information extraction capabilities. Image upsampling and super-resolution techniques have wide applications in the real world, such as image quality enhancement, satellite and remote sensing images, and security and surveillance. This paper describes and analyzes a machine learning approach to up-scaling a low resolution images using Convolutional Neural Networks. We build on previous works on single image super-resolution. We train 3 structurally similar but different models and obtain an improvement of up to 3% compared to the more common image upscaling method, Bicubic Interpolation.
Exploring Verbal Relations between &lt;i&gt;Arden of Faversham&lt;/i&gt; and John Lyly’s &lt;i&gt;Endymion&lt;/i&gt;

Several scholars, utilizing traditional reading-based methods, have highlighted intertextual links between the anonymous domestic tragedy Arden of Faversham (1590) and John Lyly’s comedy Endymion, The Man in the Moon (1588). The authorship of Arden of Faversham is fiercely contested: Brian Vickers and the present writer have argued for Thomas Kyd’s sole authorship, whereas MacDonald P. Jackson and his New Oxford Shakespeare colleagues have contended for Shakespeare’s part authorship. This article draws upon electronic corpora in order to highlight matching utterances between the Kentish tragedy and Lyly’s comedy, and suggests that the quantity of rare and/or unique verbal parallels with Endymion is indicative of a single author’s acquisitive practices. The article proceeds to explore these matches in context in order to broaden our understanding of the relations between these plays, and the composition of Arden of Faversham as a whole.&#x0D;
ADVANTAGES OF USING DIGITAL TECHNOLOGIES IN THE EDUCATIONAL PROCESS

Education is the fundamental basis of social development, and the emergence of digitalization in all spheres has significantly affected how knowledge is transmitted and acquired. Digitalization has revolutionized various aspects of human life, including education. The integration of digitalization into the educational process has opened up new opportunities for teaching and learning, improving the quality of education and turning traditional classrooms into a dynamic and interactive learning environment. This article examines the features and advantages of using digitalization in the educational process, emphasizes their impact on student engagement, knowledge acquisition and overall learning outcomes. In addition, problems and potential solutions related to the use of digitalization in education are discussed. The results emphasize the importance of effective integration strategies, professional development of teachers and infrastructural support to maximize the potential of digitalization in education.