Reviewerly/exHarmony huggingface dataset:
Interactive Music Distance Education Platform Based on RBF Algorithm

INTRODUCTION: Since the 21st century, Internet technology has been developing rapidly, and the field of education has gradually broken through the traditional offline teaching mode; utilizing the Internet mode for distance education to break the time and space constraints of education and music education is more restrictive on time and space. However, if distance education can be better utilized to promote the development of online music education, it will promote the development of music education.&#x0D; OBJECTIVES: This paper combines the past research results of the RBF algorithm, music education and distance education to study the construction of an interactive music distance education platform based on the RBF algorithm. The algorithm is used to improve the quality of the construction of the distance education platform, and the algorithm mainly optimizes the information dissemination path of distance education in the construction of the interactive music distance education platform and improves the reliability of the platform construction.&#x0D; METHODS: The RBF algorithm is used to optimize the construction of the platform, and the case method is used to study individual schools to detect the actual application of music distance education and to find the strengths and weaknesses of the interactive music distance education platform, and to improve the weaknesses to enhance the platform.&#x0D; RESULTS: The study found that constructing an interactive music distance education platform will not guarantee the quality of distance education; teachers and students can not quickly realize the effective communication between teachers and students, and some teachers can not better apply the distance education platform. However, there is still room for developing interactive music distance education platforms.&#x0D; CONCLUSION: The research of interactive music distance education platforms based on the RBF algorithm has a significant positive effect on the development of distance education, art distance education, music education and so on in China. Therefore, it is necessary to continuously improve music education by utilizing online teaching methods and teaching resources.
Multivalued Classification of Computer Attacks Using Artificial Neural Networks with Multiple Outputs

Modern computer networks (CN), having a complex and often heterogeneous structure, generate large volumes of multi-dimensional multi-label data. Accounting for information about multi-label experimental data (ED) can improve the efficiency of solving a number of information security problems: from CN profiling to detecting and preventing computer attacks on CN. The aim of the work is to develop a multi-label artificial neural network (ANN) architecture for detecting and classifying computer attacks in multi-label ED, and its comparative analysis with known analogues in terms of binary metrics for assessing the quality of classification. A formalization of ANN in terms of matrix algebra is proposed, which allows taking into account the case of multi-label classification and the new architecture of ANN with multiple output using the proposed formalization. The advantage of the proposed formalization is the conciseness of a number of entries associated with the ANN operating mode and learning mode. Proposed architecture allows solving the problems of detecting and classifying multi-label computer attacks, on average, 5% more efficiently than known analogues. The observed gain is due to taking into account multi-label patterns between class labels at the training stage through the use of a common first layer. The advantages of the proposed ANN architecture are scalability to any number of class labels and fast convergence.
Defense Method against an Attack of Incorrect Ballot Filling in a Remote Electronic Voting System

The remote electronic voting system based on homomorphic encryption and blockchain technologies is analyzed. The existing methods of protecting the voting system from an attack related to incorrect filling a ballot by the voter are studied. The method to protect against an attack of violation of the rules for filling a ballot as a whole is developed, which increases the security of voting system by ensuring secrecy of total number of votes given by voter during the verification correctness of filling a ballot.
Hybrid Optimization Based Hindi Document Summarization Using Deep Learning Technique

The proliferation of textual information today is a result of the internet's recent development, which is widely accessible to anybody, at any time. Generally speaking, several Natural Language Processing (NLP) techniques can be used to analyze the textual information that is offered on the basis of text documents. In recent years, various text summarization techniques have been implemented in English text documents but a little amount of work is carried out in Hindi text documents summarization. In this research investigation, the Coot Remora Optimization (CRO) technique based on Deep Recurrent Neural Network (DRNN) is used to summarize Hindi documents. Here, the CRO algorithm is used to train the DRNN, which is used to compute the sentence scores.The highest scored sentences are going to included in the summary. When compared to recent optimization algorithmic techniques, such as MCRMR-SSO, Graph-based_PSO, Genetic Algorithms (GA), and Political Elephant Herding Optimization (PEHO) based Deep Long Short Term Memory (DLSTM) algorithm, the developed method is shown to be superior. Additionally, three evaluation metrics such as precision, recall, f-measure are used to analyze the performance of the CRO based DRNN technique and obtained high performance.
Dense metal corrosion depth estimation

Introduction: Metal corrosion detection is important for protecting lives and property. X-ray inspection systems are widely used because of their good penetrability and visual presentation capability. They can visually display both external and internal corrosion defects. However, existing X-ray-based defect detection methods cannot present and estimate the dense corrosion depths. To solve this problem, we propose a dense metal corrosion depth estimation method based on image segmentation and inpainting. Methods: The proposed method employs an image segmentation module to segment metal corrosion defects and an image inpainting module to remove these segmented defects. It then calculates the pixel-level dense corrosion depths using the X-ray images before and after inpainting. Moreover, to address the difficulty of acquiring training images with ground-truth dense corrosion depth annotations, we propose a virtual data generation method for creating virtual corroded metal X-ray images and their corresponding ground-truth annotations. Results: Experiments on both virtual and real datasets show that the proposed method successfully achieves accurate dense metal corrosion depth estimation. Discussion: In conclusion, the proposed virtual data generation method can provide effective and sufficient training samples, and the proposed dense metal corrosion depth estimation framework can produce accurate dense corrosion depths.
Promoting data element circulation for urban digital economy development

Data element circulation, also known as data circulation, refers to the entire process of continuously collecting, processing, storing, transmitting, and using data in economic and social activities. The rapid development of information technology has made the data elements circulation an important driving force for promoting economic growth, improving production efficiency, and fostering social innovation. However, the data elements circulation also presents a series of challenges, including data value, data security, data quality, data openness, and sharing. To fully unleash the value of data resources, improve the new foundational system, stimulate market vitality, and create a data element market with significant influence, this paper conducts a thematic study on promoting data element circulation. The significance of accelerating data element circulation, the global efforts in data element circulation, and the basic requirements for data element circulation are discussed. The paper also provides recommendations for promoting data element circulation, including policy and regulatory development, infrastructure enhancement, and continuous technological innovation. Furthermore, the paper emphasizes the importance of strengthening application scenarios, such as smart city initiatives and data center services and building a robust support system for data element circulation. The proposed strategies aim to foster high-quality development of the urban digital economy and facilitate the circulation of data elements.
Deep purified feature mining model for joint named entity recognition and relation extraction

Table filling based joint named entity recognition and relation extraction task aims to share representation of subtasks in a table to extract structured knowledge. However, most of existing studies need additional labels and dedicated deep neural networks to learn shared representation, imposing heavy burdens to decoders. More seriously, almost all these models suffer from feature confusion problem, failing to capture purified task-specific features from shared representation to perform subtasks. To address these challenging problems, in this paper we propose a novel and effective Deep puRified fEAture Mining (DREAM) model for joint named entity recognition and relation extraction task, which can automatically capture purified task-specific features to improve the classification performance of subtasks. Specifically, unlike introducing additional labels or dedicated network architectures, we design a new lightweight shared representation learning (LSRL) module by the plainest labels of joint task and thus encodes context by the hybrid convolutional neural networks. Afterwards, a task-aware information bottleneck (TIB) module is proposed to explore the relation between the mutual information of the joint distribution of each subtask and its task-specific features. With the above two modules well obtain shared representation and purified task-specific features, the satisfactory classification results of both subtasks can be guaranteed. Experiment results show that the proposed model is highly effective, obtaining the promising results on three different benchmarks: CoNNL04 (general text), ADE (biomedical text) and SciERC (scientific text). For example, DREAM respectively achieves F1-scores of 78.18%, 80.28% and 44.60% in performing the relation extraction subtask on the CoNNL04, ADE and SciERC datasets. The promising performance indicates that the proposed model can be applied to many practical applications such as biomedical information extraction. The source code is publicly available at https://github.com/SWT-AITeam/DREAM.
Machine Learning Workflow

This chapter describes machine learning workflows. It starts by introducing a typical five-step workflow made of (1) data acquisition, (2) pre-processing, (3) model training, (4) model validation, and (5) model deployment. Each step is described in detail and accompanied by Python examples.
A New Separation Index and Classification Techniques Based on Shannon Entropy

Abstract The purpose is to use Shannon entropy measures to develop classification techniques and an index which estimates the separation of the groups in a finite mixture model. These measures can be applied to machine learning techniques such as discriminant analysis, cluster analysis, exploratory data analysis, etc. If we know the number of groups and we have training samples from each group (supervised learning) the index is used to measure the separation of the groups. Here some entropy measures are used to classify new individuals in one of these groups. If we are not sure about the number of groups (unsupervised learning), the index can be used to determine the optimal number of groups from an entropy (information/uncertainty) criterion. It can also be used to determine the best variables in order to separate the groups. In all the cases we assume that we have absolutely continuous random variables and we use the Shannon entropy based on the probability density function. Theoretical, parametric and non-parametric techniques are proposed to get approximations of these entropy measures in practice. An application to gene selection in a colon cancer discrimination study with a lot of variables is provided as well.
Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation

Reconstructing and segmenting cortical surfaces from MRI is essential to a wide range of brain analyses. However, most approaches follow a multi-step slow process, such as a sequential spherical inflation and registration, which requires considerable computation times. To overcome the limitations arising from these multi-steps, we propose SegRecon, an integrated end-to-end deep learning method to jointly reconstruct and segment cortical surfaces directly from an MRI volume in one single step. We train a volume-based neural network to predict, for each voxel, the signed distances to multiple nested surfaces and their corresponding spherical representation in atlas space. This is, for instance, useful for jointly reconstructing and segmenting the white-to-gray-matter interface and the gray-matter-to-CSF (pial) surface. We evaluate the performance of our surface reconstruction and segmentation method with a comprehensive set of experiments on the MindBoggle, ABIDE and OASIS datasets. Our reconstruction error is found to be less than 0.52 mm and 0.97 mm in terms of average Hausdorff distance to the FreeSurfer generated surfaces. Likewise, the parcellation results show over 4% improvements in average Dice with respect to FreeSurfer, in addition to an observed drastic speed-up from hours to seconds of computation on a standard desktop station.
Robust and Secure Hybrid Quantum-Classical Computation on Untrusted Cloud-Based Quantum Hardware

Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy/untrustworthy/unreliable vendors, to begin offering quantum computers as hardware-as-a-service at various price/performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable/trustworthy hardware. Less-trusted vendors may tamper with the results and/or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of . To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of ≈ 30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of ≈ .
Clueless: A Tool Characterising Values Leaking as Addresses

Clueless is a binary instrumentation tool that characterises explicit cache side channel vulnerabilities of programs. It detects the transformation of data values into addresses by tracking dynamic instruction dependencies. Clueless tags data values in memory if it discovers that they are used in address calculations to further access other data.
OverShard: Scaling blockchain by full sharding with overlapping network and virtual accounts

One of the primary challenges to the widespread adoption of blockchain technology is scalability. Although sharding is considered one of the most promising solutions, it still struggles to process cross-shard transactions adequately. In this paper, we propose OverShard, a full sharding approach for scaling blockchain that incorporates an overlapping network and virtual accounts. The overlapping network model enables all nodes in a shard to join multiple other shards, thereby enhancing security in a sharded system and improving communication efficiency between nodes in different shards. Moreover, the virtual accounts model facilitates transaction processing within a single shard. To the best of our knowledge, OverShard is the first model to fully address the issue of cross-shard transactions, theoretically leading to almost linear scalability with an increasing number of shards. Additionally, OverShard supports both transfer transactions and smart contract transactions in permissionless blockchain systems. We provide an analysis of the security, scalability, and performance of OverShard, then implement a prototype system of 1200 nodes with 128 shards, each joining 12 shards. Although each node's storage and communication overhead increases compared to a traditional sharding network, OverShard can scale up to nearly 128× the throughput (equal to the number of shards), with confirmation latency remaining at only one block interval.
Cut elimination by unthreading

Abstract We provide a non-Gentzen, though fully syntactical, cut-elimination algorithm for classical propositional logic. The designed procedure is implemented on $$\textsf{GS4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>GS</mml:mi> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> , the one-sided version of Kleene’s sequent system $$\textsf{G4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> . The algorithm here proposed proves to be more ‘dexterous’ than other, more traditional, Gentzen-style techniques as the size of proofs decreases at each step of reduction. As a corollary result, we show that analyticity always guarantees minimality of the size of $$\textsf{GS4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>GS</mml:mi> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> -proofs.
Cyber Security Risks in Emerging South African Smart Cities: Towards a Cyber Security Framework

Abstract Smart cities have the potential to improve the lives of citizens through the application of ICT systems that increase sustainability, service delivery, and efficiency. But, through vast connectivity and interconnected infrastructure, smart cities run the risk of falling victim to various types of cyber-attacks and information security risks. This article outlines those risks and, using a qualitative research approach, explores how a cyber security framework can inform cyber security risks in emerging South African smart cities. As such, a South African, smart city, cyber security risk framework is proposed based on the findings of interviews conducted with 17 Subject Matter Experts ( SME s). The framework is aimed at assisting smart city developers during the design, selection, and implementation of the cyber security operations within the South African context and serves as an example to other developing countries.
ASTER Data Processing by Discrete Wavelets Transform and Band Ratio Techniques for the Identification of Lineaments and Hydrothermal Alteration Zones in Poli, North Cameroon

The aim of this study is to carry out hydrothermal alteration mapping and structural mapping using ASTER images in order to identify indices that could guide mining exploration work in the Poli area and its surroundings. To achieve this, the ASTER images were first preprocessed to correct atmospheric effects and remove vegetation influence. Secondly, a lineament mapping was conducted by applying Discrete Wavelet Transform (DWT) algorithms to the First Principal Component Analysis (PCA1) of Visible Near-Infrared (VNIR) and Shortwave Infrared (SWIR) bands. Lastly, band ratio methods were applied to the VNIR, SWIR, and Thermal Infrared (TIR) bands to determine indices of iron oxides/hydroxides (hematite and limonite), hydroxyl-bearing minerals (chlorite, epidote, and muscovite), and the quartz index. The results obtained showed that the lineaments were mainly oriented NE-SW, ENE-WSW, and E-W, with NE-SW being the most predominant direction. Concerning hydrothermal alteration, the identified indices covered almost the entire study area and showed a strong correlation with lithological data. Overlaying the obtained lineaments with the hydrothermal alteration indices revealed a significant correlation between existing mining indices and those observed in the field. Mineralized zones generally coincided with areas of high lineament density exhibiting significant hydrothermal alteration. Based on the correlation between existing mining indices and the results of hydrothermal and structural mapping, the results obtained can then be used as a reference document for any mining exploration in the study area.
The Multiple Frequency Conversion Sinusoidal Chaotic Neural Network and Its Application

Aiming at the problem that the global search performance of a transiently chaotic neural network is not ideal, a multiple frequency conversion sinusoidal chaotic neural network (MFCSCNN) model is proposed based on the biological mechanism of the brain, including multiple functional modules and sinusoidal signals of different frequencies. In this model, multiple FCS functions and Sigmoid functions with different phase angles were used to construct the excitation function of neurons in the form of weighted sum. In this paper, the inverted bifurcation diagram, Lyapunov exponential diagram and parameter range of the model are given. The dynamic characteristics of the model are analyzed and applied to function optimization and combinatorial optimization problems. Experimental results show that the multiple frequency conversion sinusoidal chaotic neural network has better global search performance than the transient chaotic neural network and other related models.
Skeleton‐Guided Action Recognition with Multistream 3D Convolutional Neural Network for Elderly‐Care Robot

With the arrival of a global aging society, elderly‐care robots are becoming more and more attractive and can provide better caring services through action recognition. This article presents a skeleton‐guided action recognition framework with multistream 3D convolutional neural network. Two parallel dual‐stream lightweight networks are proposed to enhance the feature extraction ability of human action and meanwhile reduce computation. Two different modes of skeleton input video are constructed to improve the recognition accuracy by decision fusion. The backbone networks adopt Resnet‐18, the feature fusion layer and sliding window mechanism are both designed, and two cross‐entropy losses are used to supervise their training. A dataset (named elder care action recognition (EC‐AR)) with different categories of action is built. The experimental results on HMDB‐51 and EC‐AR datasets both demonstrate that the proposed framework outperforms the existing methods. The developed method is also applied to a prototype of elderly‐care robots, and the test results in home scenarios show that it still has high recognition accuracy and good real‐time performance.