Reviewerly/exHarmony huggingface dataset:
Continual Learning via Dynamic Programming

Continual learning (CL) algorithms seek to train a model when faced with similar tasks observed in a sequential manner. Despite promising methodological advancements, however, there is a lack of theoretical frameworks that enable analysis of learning challenges such as generalization and catastrophic forgetting, especially in applications where the tasks are generated continuously through a partial differential equation. To address this lack, we present a new theoretical framework that models the learning dynamics in CL through dynamic programming. Using the proposed framework, we derive a new method that adopts stochastic-gradient-driven alternating optimization to balance generalization and catastrophic forgetting. We establish conditions for convergence and show that, on CL benchmark datasets, our method achieves accuracies better than or comparable to those of existing state-of-the-art methods.
Regulatory Regulation of the Issue and Circulation of Digital Currencies in the Countries of the Modern World

In the article, the authors consider the issues of regulatory regulation of digital currencies in the countries of the modern world. The similarities and differences in the approaches of the legislators of the states of the modern world regarding the use of digital currencies are noted. Digital currency is the result of the functioning of special computer programs and platforms on the Internet. Аs a result, a special digital code is generated. At the same time, it is necessary to distinguish between cryptocurrencies, which have a decentralized nature of issuance and a method of storing information, and electronic national currencies. These digital currencies have fundamental differences in their legal status. Electronic national money, for example, in the form of a digital ruble or yuan, is just a kind of national currencies. They exist in electronic form along with cash and non-cash. In turn, cryptocurrencies are characterized by anonymity in the aspect of storing information about transactions, decentralized storage of registers. The expansion of the range of cryptocurrencies, as well as an increase in trading volumes, is important in the aspect of tax evasion and legalization of funds obtained by criminal means due to the anonymity of payments. The countries of the modern world disagree on the issue of allowing the turnover of cryptocurrencies. Starting from its ban, for example, in Russia, China, etc., to widespread, for example, in the USA, Japan, etc. The uncertain legal significance of cryptocurrencies as a surrogate of money or a security is noted. Conclusions are drawn about the development of the cryptocurrency market as a tool for reducing the cash and non-cash money supply of the US dollar and the inevitability of expanding the use of national electronic money.
Evaluation of ensemble method for multiclass classification on unbalanced data

One of the problems that often arise in classification analysis is unbalanced data. This problem causes misclassification, so that it affects the sensitivity, especially in the minority class. Unbalanced data can be handled using Synthetic Minority Oversampling Technique (SMOTE). In addition, the ensemble method is used in the classification process because it can improve classification performance. The study evaluates the ensemble method and combines it with the SMOTE to deal with the problem. The data used in this study include balance-scale, nursery, red wine quality, internet firewall, and Air Pollution Index. The study focused on random forest and adaboost in the class of ensemble methods and as a comparison to determine the good performance of this method, the k-Nearest Neighbor (KNN) and decision tree in the class of single classifier. The results are evaluated by comparing the ensemble and single classifier methods based on accuracy, sensitivity, and specificity on the data conditions before and after the SMOTE process. The evaluation of the classification result on the five datasets used shows that the ensemble method tends to provide better performance than decision tree and KNN. Data that has been processed with SMOTE produced a better sensitivity, especially in the minority class.
Artificial Neural Networks Applied to Natural Language Processing in Academic Texts

In the last decade, Artificial Intelligence (AI) has become lead on the field of information generation and processing tasks through the emergence of Machine Learning (ML), as well as the data specialist mentions Machine Learning is a master of pattern recognition, and is capable of transform a data sample into a computer program capable of drawing inferences from new data sets for which it has not been previously trained, based on artificial neural networks (ANN) processing in academic texts, which are used to identify patterns and classify different types of information, currently treated as Deep Learning (DL) which is a subset of Machine Learning, this algorithm tries to imitate the human brain by continuously analyzing data with a given logical structure, which has led to its applicability to different fields such as robotics, voice processing, artificial vision, natural language processing (NLP), with the intention to provide computer systems with the ability to learn. Natural language processing has traditionally been a complex and non-trivial task in algorithm design. Making use of AI, new thresholds are being reached in the state of the art of different problems and with constant advances in the models in use, they are being reached faster and faster.
Design and implementation of website user behavior prediction model based on FP-growth

With the development of the Internet, more and more websites appear, providing users with more and more online services. In order to improve the convenience of the page, when the user completes an operation, the next possible function is provided for the user to switch easily. This paper proposes a user behavior prediction model based on the FP-Growth algorithm. Experiments show that the model is effective for feature recommendation.
Cryptocurrency Price Prediction Using Twitter and News Articles Analysis

Due to the redefining of money and its price volatility, cryptocurrencies have become one of the most prominent phenomena in recent years. This research investigates how well public opinion on Twitter and news stories may be used to estimate cryptocurrency returns. Three models are designed and compared: LSTM based, LSTM-and-GRU-based, and LSTM and CNN-based models. Firstly, numerals and historical datasets of Bitcoins are used for all three models, which are further extended to Twitter and news datasets. An error score of 1015.17, 1106.71, and 3010.63 is obtained. Then, the proposed models are applied to the combined dataset of Twitter and news from Ethereum, and an error score of 47.85, 34.01, and 58.27 is obtained. Finally, the same methodology is applied to the combined dataset of Litecoin and obtained an error score of 9.45, 8.81, and 15.94. It is observed that LSTM with GRU generates the best results for all the datasets.
The BERT-BiLSTM-CRF Model Applied to Chinese Entity Recognition for the Science and Technology Service Field

Nowadays, the application of named entity recognition task just focuses on certain entities. When it comes to a specific vertical field, the scarcity and source of corpus become a problem. Science and technology resource service is such a vertical field without ready-made corpus. This problem has become a breakthrough point on its domestic development stage. The paper proposes a named entity recognition task which can be applied in the field of science and technology resource service, our data are based on the seeking for human resources which is a common business scenario in science and technology resource service. We use BERT model to generate word vector and combine BiLSTM's memory ability of context relationship with CRF's learning ability of annotation rules to build model. Moreover, the paper grabs the text information of recruitment websites with similar language organization structure, the same entity category and similar semantic expression, and carries out entity recognition for the “profession” entity and the “technology” entity. Finally, the model was trained in the produced data set to obtain a reasonable recognition effect, which proves the feasibility of named entity recognition in talent seeking, and can be further applied to the dialogue system of science and technology resources service to contribute to the development of its platform.
A Knowledge Distillation Method based on IQE Attention Mechanism for Target Recognition in Sar Imagery

The huge computing and storage requirements of deep con-volutional neural networks (DCNNs) limit their application on edge computing devices. In this article, we propose an attention mechanism based on the feature map quality evaluation algorithm (IQE). The knowledge distillation method based on the IQE attention mechanism uses the IQE method to identify important knowledge in the pre-trained SAR target recognition deep neural network. Then in the process of knowledge distillation, the lightweight network is forced to focus on the learning of important knowledge. Through this mechanism, the method proposed in this paper can efficiently transfer the knowledge of the pre-trained SAR target recognition network to the lightweight network, which makes it is possible to deploy the SAR target recognition algorithm on the edge computing platform. Comparison experiments with several commonly used knowledge distillation methods have proved the effectiveness of our proposed method. In addition, we also verified the performance of the lightweight network obtained by our method on the edge platform based on the K210 processor.
NLP Analysis of COVID-19 Radiology Reports in Indonesian using IndoBERT

The presence of COVID-19, a respiratory disease, can be detected through medical imaging, such as Chest X-Ray (CXR) and Computed Tomography (CT) scans. These radiology images can also show how the patient's condition progresses. Radiologists need to provide a written report for each image, so that other clinicians can use it in their decision making. In this study, we applied one of the Natural Language Processing (NLP) models called IndoBERT to analyze radiology reports of COVID-19 patients written in Indonesian. We performed two tasks, clustering to group reports by meaning and understand their content, and text classification to predict one of the five possible outcomes for each patient. We show the most frequent topics in radiology reports, and word scores in each topic. The IndoBERT model was fine tuned on a medical text, ‘Kamus Kedokteran Dorland’ in an attempt to further improve it. This proved unnecessary: on one hand, there were no additional benefits, on the other, the standard model alone achieved a very satisfactory classification accuracy of over 90 %.
Frequently Asked Question Pair Generation for Rule and Regulation Document

This paper presents a novel task to generate frequently asked question (FAQ) pairs for the rule and regulation documents. It offers an easy way for customers and employers to quickly gain knowledge of them and provides a potential corpus for question-answering robots. While previous work focuses on web texts (e.g., Wiki), we generate FAQ pairs from the formal and verbose rule and regulation documents, which is significant in real scenarios. To tackle this task, firstly, we carefully design a rules-based method to generate FAQ pairs based on structure information. Then we propose a pipeline framework for FAQ pair generation by deep learning. For experiments, we collect and annotate a Chinese FAQ pair generation dataset from documents of China Merchants Securities Co., Ltd. The results show that our method can generate proper FAQ pairs and achieve competitive performance in both automatic and human evaluation.
Efficient Data Augmentation Techniques for Some Classes of State Space Models

Data augmentation improves the convergence of iterative algorithms, such as the EM algorithm and Gibbs sampler by introducing carefully designed latent variables. In this article, we first propose a data augmentation scheme for the first-order autoregression plus noise model, where optimal values of working parameters introduced for recentering and rescaling of the latent states, can be derived analytically by minimizing the fraction of missing information in the EM algorithm. The proposed data augmentation scheme is then utilized to design efficient Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of some non-Gaussian and nonlinear state space models, via a mixture of normals approximation coupled with a block-specific reparametrization strategy. Applications on simulated and benchmark real data sets indicate that the proposed MCMC sampler can yield improvements in simulation efficiency compared with centering, noncentering and even the ancillarity-sufficiency interweaving strategy.
Improved Data Association Algorithm for Airborne Radar Multi-Target Tracking Via Deep Learning Network

Traditional data association (DA) algorithms for multi-target tracking need prior information such as target motion model and clutter density. However, this prior information cannot be timely and precisely obtained before tracking. To solve this issue, this paper proposes an improved data association algorithm for multi-target tracking via a deep learning network. First, a dataset is constructed to provide rich offline multi-target data association for network training. Then, the LSTM-DA algorithm is developed to solve the multi-target and multi-measurement matching problem based on the long short-term memory (LSTM) network. The network is composed of two LSTM layers, a masking layer, and three fully connected layers. The experimental results demonstrate that our proposed algorithm outperforms classical data association algorithms and the bi-directional LSTM network.
Psychological-educational features of musical and creative development of the student's personality

The purpose of the study is to reveal the psychological and pedagogical features of the creative development of the student in the process of music lessons. The article deals with the specifics of the development of musical and creative development of children, forms, methods, pedagogical tools. The scientific novelty of the study lies in the disclosure of the structure of the creative development of children, the stages of formation of the musical and creative potential of schoolchildren in the course of musical activity affecting the development of the student's personality are specified. As a result, it was revealed that a set of forms of musical creative activity of the student reveals his capabilities, forms values, develops personal qualities, emotional and intellectual spheres. This creates conditions for the transfer of experience and methods of creative activity in other spheres of life aimed at solving new problems to transform reality.
Research of Vehicle Detection under UAV Based on YOLOv4 Improved Algorithm

The improvement of small target detection ability determines the effect of target recognition in the field of target detection. To solve the problems of inaccurate positioning of small target detection, difficulty in feature extraction, and easy-to-miss detection, a small vehicle target detection algorithm based on YOLOv4 was proposed in this paper, namely RC_IYOLOv4, based on VEDAI, a public data set of small vehicle target under UAV. Firstly, the k-Means ++ clustering algorithm retrieves the anchor point box conforming to the VEDAI data set. Then, feature extraction was enhanced by embedding RFB receptive field module and amplifying small target receptive fields by replacing the SPP module with RFB. Finally, this paper adds the CBAM attention mechanism module. It uses one-dimensional convolution to replace the improved combination of complete connection and parallel connection in its interior with focusing on learning the feature representation of small targets. Based on the improved methods above, the experimental results show that the proposed improved algorithm RC_IYOLOv4 improves 4.98% in the VEDAI data set MAP@0.5 compared with YOLOv4. Finally, in the PASCAL VOC data set commonly used for target detection, MAP@0.5 improves by 2.27%.
SPACL: Shared-Private Architecture Based on Contrastive Learning for Multi-domain Text Classification

With the development of deep learning in recent years, text classification research has achieved remarkable results. However, text classification task often requires a large amount of annotated data, and data in different fields often force the model to learn different knowledge. It is often difficult for models to distinguish data labeled in different domains. Sometimes data from different domains can even damage the classification ability of the model and reduce the overall performance of the model. To address these issues, we propose a shared-private architecture based on contrastive learning for multi-domain text classification which can improve both the accuracy and robustness of classifiers. Extensive experiments are conducted on two public datasets. The results of experiments show that the our approach achieves the state-of-the-art performance in multi-domain text classification.
Augmented Reality in Telecom Industry: Concepts, Technologies and Applications

Augmented reality, as an important end of merging virtual objects and the real world, has been widely used in the Internet, games, e-commerce, and other fields. The maturity of AR technology has brought a broad market space and gained the attention of researchers and companies. As an important ICT technology industry, the tele-com sector also needs AR technology for its complex business scenarios and issues. Unfortunately, few studies focused on the application of AR technology in the telecommunication field. This paper starts from the concept of AR technology, introduces the key technologies of AR and details its application cases in the field of telecommunications. The work analyzes how AR technology solves the operation and maintenance work problems in the telecommunications field from two aspects: auxiliary inspection and equipment maintenance. It introduces in detail how AR technology improves the operation and maintenance work efficiency in the telecommunications field.
Towards Situational Process Management for Professional Education Programmes

Designing and running professional education programmes involves various processes that responsible parties must manage. The knowledge gathered during development and with each new programme iteration is seldom externalized and it cannot be guaranteed that it will be applied in the next programme or iteration. This work-in-progress paper proposes a situational process management approach for professional education programmes. The basis for our approach is a modelling language to describe the high-level perspective of such programmes. Processes, best practices and existing content elements can be extracted from these models and added to a knowledge base alongside situational factors in which these items apply. If a relevant situation is observed in a model representing a professional education programme, the applicable items from the knowledge base are suggested to users. Our approach allows persisting the knowledge gained while developing/running professional education programmes and makes it accessible to others.
A Unifying Probabilistic Framework for Partially Labeled Data Learning

Partially labeled data learning (PLDL), including partial label learning (PLL) and partial multi-label learning (PML), has been widely used in nowadays data science. Researchers attempt to construct different specific models to deal with the different classification tasks for PLL and PML scenarios respectively. The main challenge in training classifiers for PLL and PML is how to deal with ambiguities caused by the noisy false-positive labels in the candidate label set. The state-of-the-art strategy for both scenarios is to perform disambiguation by identifying the ground-truth label(s) directly from the candidate label set, which can be summarized into two categories: 'the identifying method' and 'the embedding method'. However, both kinds of methods are constructed by hand-designed heuristic modeling under considerations like feature/label correlations with no theoretical interpretation. Instead of adopting heuristic or specific modeling, we propose a novel unifying framework called A Unifying Probabilistic Framework for Partially Labeled Data Learning (UPF-PLDL), which is derived from a clear probabilistic formulation, and brings existing research on PLL and PML under one theoretical interpretation with respect to information theory. Furthermore, the proposed UPF-PLDL also unifies 'the identifying method' and 'the embedding method' into one integrated framework, which naturally incorporates the feature and label correlation considerations. Comprehensive experiments on synthetic and real-world datasets for both PLL and PML scenarios clearly demonstrate the superiorities of the derived framework.
DualHead for One-stage Object Detection Networks with Receptive Field Enhancement

The ordinary detection head has a simple structure in one-stage object detection networks, leading to its receptive field being too small to completely cover the feature region of some objects with a large aspect ratio. Furthermore, the detection precision of networks is also reduced. To solve this problem, we propose a dual detection head, called DualHead, to enhance the receptive field and improve the detection precision. The DualHead is composed of two parallel sub-heads with different receptive fields: the sub-head with a small receptive field extracts dense small range features, and the other with a large receptive field extracts sparse large range features. By fusing the feature maps output by two sub-heads with the proposed channel-wise reorganization convolution fusion (CRCF) module, the receptive field of DualHead is about 4.5 times larger than that of the ordinary head, so that it is enough to cover the whole feature region of all objects to be detected. The experiments on the MSCOCO 2017 dataset show that DualHead improves the detection precision AP by 1.2% and 0.9% of ATSS with ResNet-50 and Swin-T as the backbone.
A method of evaluation index of author’s academic influence based on author Citation Network

To overcome the shortcomings of PageRank algorithm that neglects the time factor in author evaluation, a new method for evaluating author's academic influence is proposed. Firstly, the PageRank algorithm is applied to calculate the author's PR value in academic social network to distinguish the author's contribution. Secondly, timePageRank algorithm (abbreviated as T-PR) is given by incorporating the citation time heterogeneity of the author's paper into PageRank algorithm. Finally, the author's literature data in the field of information science is taken as an example to make an empirical analysis. By comparing with PageRank algorithm, it is found that T-PR algorithm can not only identify the core authors in this field, but also distinguish the ranking of authors'influence better, and the evaluation results are more objective and reliable.