Reviewerly/exHarmony huggingface dataset:
A Multi-scale Convolutional Neural Network for Skeleton-Based Human Action Recognition with Insufficient Training Samples

Human action recognition (HAR) has been attracting researchers’ attentions because of its broad application prospect and significant research value. Although researchers have made great achievements, dealing with the various duration of the actions and insufficient training samples is still a big challenge for HAR. To address the two issues, a new human action representation method and a multi-scale deep neural network (DNN) model are proposed. The contributions are threefold. First, a new data structure which is named as joint motion matrix (JMM) is put forward for HAR description. By using the JMM, the motion information loss problem which is caused by spatio-temporal occlusion can be effectively solved. Second, a data augmentation method based on the characteristics of human physiological structure and human actions is proposed to generate action samples with brand new motion information. Third, the combined usage of spatial pyramid pooling (SPP) layer and global average pooling (GAP) layer not only enables the DNNs to receive multi-scale inputs, but also provides an effective way for the DNNs to extract more detail global features. The proposed method was evaluated on two small but challenging datasets, Florence 3D Actions and UTKinect-Action3D. The experiments show that the proposed data augmentation strategy is highly effective for DNN training, and the proposed method has strong capacities in multi-scale feature learning and computational cost reductions. The proposed approach achieves well performances on the two datasets with accuracies of 94.27% and 96.83%, which are the highest accuracies in the comparisons.
A Study of Marx and Engels' Main Viewpoints on Peasants

Marx and Engels early discussed the ignorance and conservatism of English, French and German peasants in the production process, and held a relatively negative attitude towards peasants. With the continuous development of the revolutionary process, Marx and Engels began to have a fundamental change in their attitude towards peasants, gradually realized that peasants played an indispensable role in the revolutionary struggle of the working class, and put forward the idea of worker peasant alliance and agricultural cooperatives to transform peasants.
Improvement and Application of Image Segmentation Algorithm for Outdoor Augmented Reality

The traditional image recognition process mostly occurs in indoor scenes with relatively stable environment, and the current development of augmented reality tends to apply to outdoor scenes, such as the identification of buildings, statues and other objects in the tourism industry. Aimed to cope with the problem that the augmented reality effect of the ARToolKit system is difficult to be achieved in the outdoor natural scenes because of the wrong segmentation due to the influence of real-time changing illumination, occlusion and other factors, an improved GrabCut algorithm based on histogram equalization is proposed. Histogram equalization uses non-linear stretching of the image to improve image contrast. The boundary function of GrabCut algorithm is averaged to reduce the error segmentation of edge pixels, improve the edge integrity of the target image, and achieve accurate image segmentation. The experimental results show that the proposed method has high real-time performance and stability in complex real-world scenarios, and improves the processing performance of ARToolKit augmented reality system in outdoor scenes.
Research on the Framework of Confederate Modelling System

In the practice process of system modelling, due to the need of system engineering related ideas and technologies, combined with related professional and expert knowledge, the application degree in actual engineering and scientific research is low. In the process of system modelling, massive human resources are needed to participate, and in the process of system simulation, quantities of calculations, vast coupling calculations are needed between different levels of the system and different subsystems at the same level. At the same time, the system operation data of different subsystems and different levels, especially the operation mode and data of the underlying components, involve the core business secrets and user privacy of different manufacturers, and it is often difficult for digital twin entities to obtain real and effective data during operation. In this paper, we propose a modelling and simulation architecture of the Confederacy system and sort out the problems and challenges of privacy protection, model confirmation and computing unloading of the system model.
A Wireless Covert Channel Based on Syndrome-Trellis Codes

In view of the threats to wireless communication security caused by technical means such as wireless eavesdropping, the state-of-the-art scheme of wireless covert channel modulates the artificial noise from secret messages and superimposes it on normal signals for transmission which avoids detection by unauthorized third parties. This type of wireless covert channel is undetectable. However, the transmission of secret messages is easily interfered. This paper proposes a wireless covert channel scheme based on syndrome-trellis codes. By constructing the syndrome-trellis codes, the sender embeds secret messages in the cover messages to generate the stego messages undetectably. The receiver extracts the secret messages from the stego messages through the corresponding parity check matrix of the syndrome trellis codes. The simulation results show that the proposed scheme has an improvement in reliability while maintaining good undetectability.
SA-NMS: Multi-document Summarization with Self-attention and Non-maximum Suppression Selection

In the extractive multiple documents summarization (MDS) field, most existing works input all related documents simultaneously to generate the summary. As a result, they usually suffer from the domination of one document (because of length or other factors) which jeopardizes the completeness of the summary. We propose a novel neural network model, Self-Attention with Non-Maximum Suppression (SA-NMS), inspired from the perspectives of human reading habits and object detection to solve the problem. Our model trains a self-attentive neural network at the single document level to get a sentence prominence score in its document, treating each document with the same weight to avoid the domination of one document. Then the NMS, an algorithm widely used in object recognition but first adopted in the field of NLP, is implemented as a strong filter to reduce redundancy. The experimental results show that our SA-NMS model not only is easy to train but also performs competitively on the small DUC-2004 dataset (50 pairs in total) and outperforms the extractive approaches on ROUGE scores on the large-scale Multi-News dataset.
Network Architecture Design of Big Data Real-Time Processing System

At present, most real-time big data processing systems use the combination of software and hardware, dedicated bus or network transmission for data exchange. The network is the core communication link, and its transmission efficiency and reliability often determine the transmission efficiency and reliability of data. Efficiency of the entire system. In addition, when designing the network architecture, not only should the system software architecture be tightly integrated, but also the rationality, high availability, high redundancy, high security and scalability of network data transmission should be comprehensively considered..The teacher put forward higher design requirements. By analyzing and sorting out the relevant elements of network construction, the main principles that need to be considered in network architecture design are given. At the same time, it analyzes the common problems and faults when the network architecture design is not fully considered, and finally gives the solutions to the related problems. Methods and guidance.
Identification of Walkie-Talkie Individual Based on Precise Measurement of Frequency and Discriminant Analysis

In order to improve the accuracy of walkie-talkie individual identification, feature extraction of walkie-talkie signals is carried out. In this paper, five walkie-talkies of the same type and production batch are used as signal sources for experiments. By performing FFT on the signals sent by the walkie-talkies when there is no human voice, it is found that there is a stable frequency deviation in the transmission source of the walkie-talkies and the causes of such deviation are briefly analyzed. In order to make this deviation separable, the original signal spectrum is moved to zero-frequency, filtered by a low-pass filter and resampled. Then the normalized FFT peak point is extracted as the fingerprint feature of the walkie-talkie, and the discriminant analysis classification method is used to classify this feature. Experimental results show that the identification accuracy of this method reaches 100% when there is no interference of other factors, and it has high identification accuracy in multiple interference environments. And through the experiment under the condition of very low SNR, it is further verified that the system has strong anti-noise interference ability, which fully proves the effectiveness of this method.
A Novel Method for Diagnosis of Breast Cancer Tumors Based on Random Forest

GLOBOCAN 2020 cancer data shows that female breast cancer has become the most common cancer over lung cancer for the first time.As a disease threatening the life safety of women all over the world, how to improve the accuracy of breast cancer diagnosis and help patients get treatment as early as possible is of great importance.This paper introduces a new random forest-based breast cancer diagnosis method (NRFM), using the average radius, average texture, average circumference and other 30 indicators in the nucleus of breast mass as characteristics, to diagnose the benign and malignant breast cancer.NRFM proposed to randomly miss a certain percentage of breast cancer data, using random forest regression to fill in the experiment proved that using the method proposed in this paper, when the proportion of missing data reached 50%, the accuracy of breast cancer diagnosis will be as high as 96.85%.Experiments show that NRFM is easy to understand, convenient to operate, and has practical application value, which can assist doctors to improve the accuracy of breast cancer diagnosis.
A novel Bayesian federated learning framework to address multi-dimensional heterogeneity problem

&lt;abstract&gt;&lt;p&gt;Federated learning (FL) has attracted a lot of interests as a promising machine learning approach to protect user privacy and data security. It requires the clients to send model parameters to the server rather than private datasets, thus protecting privacy to a significant extent. However, there are several types of heterogeneities (data, model, objective and systems) in FL scenario, posing distinct challenges to the canonical FL algorithm (FedAvg). In this work, we propose a novel FL framework that integrates knowledge distillation and Bayesian inference to address this multi-dimensional heterogeneity problem. On the client side, we approximate the local likelihood function using a scaled multi-dimensional Gaussian probability density function (PDF). Moreover, each client is allowed to design customized model according to the requirement through knowledge distillation. On the server side, a multi-Gaussian product mechanism is employed to construct and maximize the global likelihood function, greatly enhancing the accuracy of the aggregated model in the case of data heterogeneity. Finally, we show in extensive empirical experiments on various datasets and settings that global model and local model can achieve better performance and require fewer communication rounds to converge compared with other FL techniques.&lt;/p&gt;&lt;/abstract&gt;
A Planar Object Tracking Algorithm with Balanced Speed and Accuracy

The planar object tracking algorithm are widely used in robot navigation, intelligent video surveillance, human-computer interaction and augmented reality. For the given object in the first image of the video, the task of the planar object tracking algorithm is to mark the location of the object in subsequent images. In recent years, the research on planar object tracking technology has made great progress, but most tracking algorithms with strong robustness and high accuracy have a slow speed. In this paper, a planar object tracking algorithm combining GIFT feature descriptor and ORB feature descriptor is proposed to solve this problem. The algorithm first uses the faster tracker based on the ORB feature descriptor to track, and if the difference between the obtained tracking result and the previous frame tracking result is larger than a certain threshold, the tracker based on the GIFT feature descriptor will be used. In particular, the threshold can be adjusted to balance the speed and accuracy of the algorithm. Experimental results on POT dataset show when the threshold is 15, the algorithm has a good performance.
Optimization of Cloud Datacenters’ Benefit Using Monte Carlo Algorithm

Data centers are very rapid growing structures with outstanding contribution to the world’s energy generation and consumption. The use of renewable energy sources to supply power has received much attention due to their many environmental benefits. However due to the variable nature of the output power of renewable energy sources, providing reliable and stable power for the data center faces many challenges. Therefore, in this paper, an intelligent energy scheduling approach for providing reliable power for datacenters using renewable energy resources is proposed. The purpose of the proposed intelligent algorithm is to minimize datacenters’ expenses and to maximize the benefit of all renewable and non-renewable energy resources and the battery energy storage system. To manage the load demand of the datacenter and all the distributed energy resources optimally, the multi-agent hour-ahead energy scheduling problem is modeled as a finite Markov decision process (MDP). The entities of the grid comprising datacenters and power resources are considered as intelligent agents. The scheduling decisions of the agents are obtained through the newly developed framework of the model-free reinforcement algorithm to compute an optimal policy. Based on the observation of the system using a non-optimal policy, the optimal policy is achieved. Finally, the performance of the proposed algorithm is verified using real power-grid data. Moreover, the simulation results indicate the presented framework can significantly improve the benefit of the cloud data center and can be applied in real-time.
Progressive search personalization and privacy protection using federated learning

Abstract Personalized search comes under recommendation systems which provide more user‐focused query results. Applications such as movie recommendations, document recommendations, and so forth are using machine learning algorithms to suggest user‐tailored content. These algorithms train on historical user query data and predict documents based on user preferences. The existing methodologies on personalization have shown two main limitations: privacy concerns over the usage of personal data and a lack of efficient personalizing strategies for better accuracy and overall standardization. In this paper, we propose a novel federated learning (FL) algorithm for personalized search results using temporal characteristics of the user query data. Individual user query data is used for developing specific client models while the aggregate of such models is developed for general search suggestions. The introduction of time‐series interpretation of queries provides for larger training data as well as a better understanding of a user's current needs and intent. The proposed algorithm is validated using the AOL4PS dataset and is evaluated on the efficiency of personalization, and the amount of data and time required to achieve the results. Its performance is compared with existing state‐of‐the‐art personalization algorithms that utilize deep learning and FL. Mean reciprocal rank (MRR) is the primary metric for measuring the algorithm's performance. After training for 35 federated rounds, the server model yielded an MRR score of 0.8638 while the client models were able to yield an average MRR score of 0.9308.
Addressing the Curse of Missing Data in Clinical Contexts: A Novel Approach to Correlation-based Imputation

Clinical data are essential in the medical domain. However, their heterogeneous nature leads to many data quality problems, notably missing values, which undermine the performance of Machine Learning-based clinical systems. Hence, there has been a growing interest in strategies that address this challenge in order to build trustworthy systems to improve the quality of care and benefit clinical decision-making. In particular, missing value imputation is a common approach. This paper proposes three novel imputation techniques that leverage correlation in an innovative manner by exploring the relationship between values and missingness patterns. Experiments were carried out on three publicly available datasets, under three missingness mechanisms with different missing rates, and on two real-world medical datasets. The imputation precision and the classification performance of the proposed techniques were evaluated in a comprehensive comparative study, which included diverse existing methods. The developed techniques outperformed state-of-the-art methods on several assessments while overcoming current flaws shared by correlation-based imputation strategies in real-world medical problems.
Unifying Models for Word Length Distributions Based on Types and Tokens

Word length studies have been one of the central issues in Quantitative Linguistics for a long time. Most models were constructed for very specific purposes, i.e. the individual models apply only to a specific language, only to token counts or only to type counts. The present paper takes up the challenge of developing unifying models which account for both type and token frequencies of a moderately large sample of languages (eight Indo-European and two non-Indo-European languages). We introduce three models which can be well fitted to all our data: the exponentiated Hyper-Poisson distribution, the generalized gamma and the Sichel distribution. We also discuss the possibility of interpreting the model parameters linguistically.
A web application for reasoning on probabilistic description logics knowledge bases

Abstract The aim of the Semantic Web is making information and resources from the Web automatically processable by machines. Usually, the uncertainty characterizing much of this information is addressed by means of a probabilistic semantics. Following the vision of a “Probabilistic Semantic Web”, a plethora of probabilistic semantics have been proposed: some of them change the syntax and/or the semantics itself of the knowledge representation language, others allow one to annotate axioms of a knowledge base with a probability value. Among the latter, the DISPONTE semantics exploits probabilistic annotations to extend query answering with the capability of returning the probability of a query being true in a domain. In order to promote the adoption of Probabilistic Semantic Web we first developed BUNDLE, a framework that can exploit different underlying (probabilistic and non‐probabilistic) reasoners to perform probabilistic inference under the DISPONTE semantics. In this paper we present a web application for BUNDLE, to show how DISPONTE is easily usable even in already established applications and systems. It allows users to query a DISPONTE knowledge base written or uploaded directly in the application interface by using just a web browser, without the need to install any software on their machine. It is accessible on the web at https://bundle.ml.unife.it/ and also provides some examples for familiarizing with the application. The results of a usability evaluation involving human participants are also reported, showing the relevance and the practical impact of the tool and possible ways for improvement.
An integral approach for complete migration from a relational database to MongoDB

Today, computing has become an obligation in the lives of individuals and institutions alike. This magical sector uses and develops very rich, important, and sensitive tools and solutions, which make everyone's life easier. Computers with their tools have become indispensable and they have transformed the world into a small town. These tools revolve around data, the type, nature, and size of which continually increases up to zeta bytes of storage capacity, thus creating BigData. Considering that Relational databases present difficulties in the management of BigData, and Knowing that companies want to keep their data accumulated over decades of exercises and studies on the market, and also taking into account the cost impact if they kept both systems in terms of software, technical support and user training; therefore it is legitimate to find a reliable way to migrate their data from the relational system to a NoSQL system, which designed specifically to handle BigData. In this optic, several studies and approaches have been developed, but they present a lack or weaknesses in the treatment of the main components of the database, which we are going to deal with in our new and integrated approach to migration from the relational database system to MongoDB as a NoSQL system. In this article, we will present our contribution by developing a complete concept of our approach, starting with an introduction, which will be followed by a discussion of what other researchers have done in this direction, then we move on to a phase of analysis and modeling to develop the models and meta-models of the two systems: source and destination, also during this section we also present our analysis and modeling methodology, to present our global approach which divides its treatment into three axes, each of which processes a part of the RDBMS with a particular nature of data: data stored in tables, data carried on the structure of the RDBMS and data coming from the semantics of relational databases. The overall architecture of our approach, which we named ''TMSDRDND'', is formed by two layers: ''TSRSNLayer'' which deals with the transformation of the structure and the transfer of semantic data, and the ''MDRSNLayer'' layer, Which takes care of data migration using an ETL to be developed according to a specific concept and architecture and exploiting the results of the first layer.
Bayesian Model Choice for Directional Data

This article is concerned with the problem of choosing between competing models for directional data.In particular, we consider the question of whether or not two independent samples of axial data come from the same Bingham distribution.This is not a straightforward question to answer, due to the intractable nature of the parameter-dependent normalizing constant of the Bingham distribution.We propose three different methods to perform this task within a Bayesian framework, and apply the methodology to a real dataset on earthquakes in New Zealand.R code to run our methods is available in online supplementary materials.