Reviewerly/exHarmony huggingface dataset:
Bayesian Network Structure Learning by Ensemble Learning and Frequent Item Mining

Aiming at the common problem of low learning effect in single structure learning of a Bayesian network, a new algorithm EF-BNSL integrating ensemble learning and frequent item mining is proposed. Firstly, the sample set is obtained by sampling the original dataset using Bootstrap, which is mined using the Apriori algorithm to derive the maximum frequent items and association rules so that the black and white list can be determined. Secondly, considering that there may be wrong edges in the black and white list, the black and white list is used as the penalty term of the BDeu score and the initial network is obtained from the hill climbing algorithm. Finally, repeat the above steps 10 times to obtain 10 initial networks. The 10 initial networks were integrated and learned by the integrated strategy function to obtain the final Bayesian network. Experiments were carried out on six standard networks to calculate <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> score and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </math> . The results show that the EF-BNSL algorithm can effectively improve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> score, reduce <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">D</mi> </math> , and learn the network structure that is closer to the real network.
An improved cooperation search algorithm for the multi-degree reduction in Ball Bézier surfaces

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly.
An Authorship Identification Empirical Evaluation of Writing Style Features in Cross-Topic and Cross-genre Documents

In this paper, an investigation was done to identify writing style features that can be used for cross-topic and cross-genre documents in the Authorship Identification task from 2003 to 2015. Different writing style features were empirically evaluated that were previously used in single topic and single genre documents for Authorship Identification to determine whether they can be used effectively for cross-topic and crossgenre Authorship Identification using an ablation process. The dataset used was taken from the 2015 PAN CLEF Forum English collection consisting of 100 sets. Furthermore, it was investigated whether combining some of these feature sets can help improve the authorship identification task. Three different classifiers were used: Naïve Bayes, Support Vector Machine, and Random Forest. The results suggest that a combination of a lexical, syntactical, structural, and content feature set can be used effectively for cross topic and cross genre authorship identification, as it achieved an AUC result of 0.837.
Sharing entanglement of the Werner state by arbitrarily many independent observers

The problem of sharing quantum correlations is an interesting problem in the study of quantum information theory. Silva et al. proposed the study of sharing quantum nonlocality at first. They studied the fundamental limits on nonlocality, asking whether a single pair of entangled qubits could generate a long sequence of nonlocal correlations. At the same time, the sequential scenario was also introduced first, in which Alice and Bob each have half of a pair of entangled qubit states. The first Bob measures his half and then passes his part to a second Bob who measures again and so on. Obviously, even partial preservation of entanglement in a shared state in spite of a few sequences of local operations performed by the sharing parties can be important for information processing schemes in which entanglement is utilized as a resource. Thus, the problem of sharing quantum entanglement has also been extensively investigated. Recently, Srivastava et al. proved that there exist a class of T-states whose entanglement can be shared by arbitrarily many independent observers in [&lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1103/PhysRevA.105.062413"&gt;&lt;i&gt;Phys. Rev. A&lt;/i&gt; 2022 &lt;b&gt;105&lt;/b&gt; 062413&lt;/ext-link&gt;]. Here, we want to find whether there are other entangled states that can be shared entanglement arbitrarily many times. In this paper, we consider the problem of sharing quantum entanglement when the initial shared state is a two-qubit entangled Werner state. The goal is to maximize the number of Bobs that can share entanglement with a single Alice. By suitably choosing the entanglement witness operator and the unsharp measurement settings by the Bobs, we prove that there exist two-qubit entangled initial shared Werner states whose entanglement can be detected by arbitrarily many sequential observers Bobs with a single Alice. Then, we also consider the special case of the Werner state, that is, the maximally entangled state as the initial shared state. In this case, its entanglement can also be witnessed arbitrarily many times, and the number of Bobs increases with the decrease of parameter.
Application of Blockchain Technology in Human Resource Management

The application of blockchain in finance and other related areas is quite evident, but the role of blockchain in human resource management is not well discussed so far. The objective of this chapter is to examine the pertinent part of blockchain technology in different areas of human resource management, ranging from the recruitment and selection process for certificate verification and skill mapping to payroll processing and employee data protection. The chapter concludes with the challenges behind the implementation of blockchain technology in the area of human resource management.
Medical Image Super Resolution by Preserving Interpretable and Disentangled Features

State of the art image super resolution (ISR) methods use generative networks to produce high resolution (HR) images from their low resolution (LR) counterparts. In this paper we show with the help of interpretable saliency maps that generative approaches to ISR can introduce undesirable artefacts which can adversely affect model performance in downstream tasks such as disease classification. We propose a novel loss term that aims to maximize the similarity of interpretable class activation maps between the HR and LR images. This not only preserves the images' explainable information but also leads to improved performance in terms of super resolution output and downstream classification accuracy. We also incorporate feature disentanglement that plays an important role in our method's superior performance for super resolution and downstream classification task.
Transformations of everyday cognition in modern society

The focus of the study is on analyzing the transformations of everyday cognition in modern society on the basis of the epistemological paradigm by V. S. Stepin. This paradigm is a productive research program that has enabled us to structure everyday cognition and systematize its functions. In the present study the transformations of the object, subject, goals and means of everyday cognition in the modern digital society have been identified. The specific character of the influence of digital technocode on the subject of everyday cognition has been considered. It is concluded that the epistemological paradigm by V. S. Stepin is a fruitful methodological basis for the study of everyday cognition and its transformations in modern society.
Bringing Museums to Juvenile Prison Inmates through Virtual Reality

Museums worldwide, as institutions, are evolving and supporting new values and goals, such as accessibility, sustainability, and above all inclusion. Yet, for several different reasons, some people may not have the opportunity, the capability, or simply the will and motivation to visit, therefore not only missing the chance to experience the exhibits, but also to be involved in the cultural and social mission of art itself. To respond to their impossibility to reach museums, we propose a moving version of a museum in an electric bus, that can bring the experience to those people through virtual reality. The use case presented is related to juvenile prison inmates, and the validation of the benefits of the approach is carried on following the practices of user experience studies and experimental psychology.
Exploring Resource Distribution Networks in Virtual Environments

Finding optimal resource distribution networks implies the iterative sampling of network topologies that avoid overlapping paths and crossings with obstacles. This paper presents an approach to aid the search for minimal networks by visualizing the deployment of networks in virtual environments through a VR headset, enabling the possibility of natural and intuitive navigation (translation and rotation) to visualize the topology of the optimal resource distribution in a virtual environment. Our proposed approach has the potential to enable the interactive design of distribution networks in virtual domains. We present several examples showing the feasibility of our system.
Mixed Reality with Hardware Acceleration: implementing the multimodal user interface vision

A Mixed Reality prototype application is proposed using the HoloLens Head Mounted Display device delegating AI tasks to edge or cloud systems. Processing and Battery life limitations of Head Mounted Display (HMD) devices do not allow these tasks to be executed for substantial periods of time without having an assistive external infrastructure to computationally support this process. Additionally, persistent and time sensitive AI procedures are delegated to achieve reliable and robust results. The architecture delegates the detection process to NVIDIA Jetson TX2, a GPU-accelerated, embedded and power-efficient device. We apply YOLO as the deep learning algorithm for object detection and use trained models on the Microsoft’s Common Objects in Context (COCO) dataset. We project visual and auditory information about the discovered objects.
Using Developer Factors and Horizontal Partitioning to Recommend Bug Severity in Open-Source Software Projects

Software testing is performed during engineering of software to uncover the faults hidden in software systems. This data about the faults and failures is recorded in problem report forms. In addition to recording the symptoms of the problems, the problem report forms record severity of the fault. This severity of the potential failure refers to the impact on user or business. Usually, this severity is assigned by humans responsible for testing of software. Given the fact that faults in the software are result of human errors, literature has reported the use of developers' information to predict faults in software. Using the same intuition, this paper investigates the use of developers' information (along with the static code metrics) to determine the extent of high severity faults in software. To this end, three open-source software projects are studied in this paper and the code files are identified that have a chance of high severity faults. This recommendation can help testing teams identify fault prone files and label code files with high, medium, or low chance of severe faults. Static code metrics for the three projects are collected through SonarQube and developer related factors are extracted from the GitHub through custom scripts. This data is used to develop machine learning classifiers and code files are labelled as high, medium, and low number of severe faults. In order to further study the relationship between the number of developers and the files with severe faults, association between the developer factors and the presence of high severity faults is found. Study of this association reveals that if there are more developers working on a file, the file is more probable to have critical faults.
The State of Practices in Requirement Elicitation: An Improved Methodology for Pak Software Industry

The discipline of Requirement Engineering is associated with great number of problems, starting from the elicitation phase to the implementation of these requirements in the software industry. Although many approaches have been proposed by the researchers, but very small number of approaches are used in software industries. It is known that requirements which are not well understood and unrestricted, changes to the scope of requirements leads numerous software projects to failure. So, good requirements are very important for project success. Paper is to explore about state of approaches regarding requirement gathering by closely exploring current approaches used by the practitioners or professionals. Aim of this paper is to focus on approaches used in software industry for elicitation purpose and the challenges faced during the process of elicitation. For this we carried out survey, based upon interviewing 20 professionals from 10 different Pakistani Information Technology companies. We analyzed these findings by qualitative methods. This includes semi structure interviews for data gathering. Various results came out from these findings. Group interaction approaches together with (brainstorming and meetings) are considered as most favorable approaches for elicitation purpose, except in small projects. Lastly, we recognized some challenges related to stakeholders. Challenges comprise of complication in prioritizing and capturing the needs of the customers. These findings need to be demonstrated in context of this research. We concluded that findings of the survey professional's knowledge should be to the interest of software industry; knowledge should be examined in professional context.
Event Recommendation Based on Heterogeneous Social Network Information and Time Information

In recent years, event-based social networks have developed rapidly, and event recommendation has attracted more and more attention. At present, for event recommendation, it is centered on the event, and aims to help users get the events which they are interested in from a large number of events. However, compared with traditional recommendation problems, event recommendation has many challenges. First of all, there is no obvious explicit rating of users’ response to events, but implicit feedback. Secondly, the recommendation of events has heterogeneous social network relations. At the same time, most users participate in few events, which leads to a very serious data sparsity problem. In order to address these challenges and improve the effectiveness of event recommendation, this paper proposes an event recommendation model that integrates users’ online and offline heterogeneous social network information and time information. The model uses Bayesian personalized ranking as the framework to process the implicit feedback information of users and events, and simultaneously combines online and offline social network information and time information to model together to improve the accuracy of recommendation. Experimental results based on real data sets show that the performance of the proposed model is better than other methods.
Capsule Network with Inception Structure for Image Classification

Convolutional Neural Networks (CNNs) are popular in deep learning, especially in computer vision. But CNNs are poor at extracting spatial information, such as position and direction of entities in an image. A recently proposed model called Capsule network could effectively learn the spatial relations among entities, but it has weak feature extraction ability. The inception structure could extract multi-scale features. So this paper modifies the inception-v1 structure and adds it into Capsule network to strengthen the feature extraction ability. In the modified inception-v1, the max pooling branch is removed to reduce the loss of feature information. And Batch Normalization (BN) layer is added after each convolution to accelerate convergence and reduce over fitting. To organize categories information, a fully connected layer is added after digit Capsules. This paper conducted experiment on two public datasets, and the results show that the proposed model outperforms the original model in accuracy.
Research on cross-project software defect prediction based on feature transfer method

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities.
RPSigmoid: A Randomized Parameterization for a Sigmoidal Activation Function

Activation functions are integral components in neural networks because they are the calculations that each neuron performs on their inputted data before outputting it to the next neuron to help the neural network match the ground truth of the data sooner, thus converging faster. However, popular activation functions are not parameterized and those that are, have too few parameters, therefore lacking the ability to fully train the shape of the activation function. This paper introduces RPSigmoid, an activation function based on the Sigmoid function, and with four additional parameters which represent the vertical stretch factor, horizontal stretch factor, angularity, and slope of the asymptotes (which might be horizontal or oblique) of the sigmoidal curve. These parameters are randomized within a range before training and their values are updated along with other neural network parameters during backpropagation. Affirmative results of RPSigmoid present neural network training with a low-resource approach to yield impressive training results.
Graph Neural Network Recommendation Method Based on User Behavior

In recent years, the recommendation field has gradually started to combine GNN-like approaches to address the challenges. The Neural Graph Collaborative Filtering (NGCF) framework has made a preliminary attempt to extract structural knowledge in model-based collaborative filtering based on graph convolution with message passing mechanisms, opening up new research possibilities. However, the NGCF framework does not consider the semantic information in the topology and only constructs a single heterogeneous graph.
A Network Traffic Classification Model Based On XGBOOST_RFECV Feature Extraction

Network traffic plays a crucial role in the interaction and transfer of information in the network area, which contains a large amount of information with important value. Therefore, network traffic classification is essential for network management, security monitoring and intrusion detection. However, the performance of network traffic classification is greatly affected by the extremely unbalanced datasets which are publicly available. In order to solve the problem of low accuracy of minority class classification. In this paper, we used SMOTEENN as the balanced method and XGBOOST_RFECV was used for feature selection. Subsequently, the neural network model (1DCNN_BiLSTM) was used for training and verification. The experimental results show that this method can effectively solve the problem of imbalanced data category, which has certain reference significance for the research of network traffic classification technology.
Research of Network Hotspot Events Joint Extracting Based on BERT-CNN-CRF Model for Internet Public Opinion

Public emergencies affect the vital interests of a large number of citizens and are extremely concerned because of their complexity and strong influence. Early warning of public opinion is an important part of reducing secondary harm of public emergencies and ensuring social stability. There are problems of insufficient ability to capture the semantics of trigger words and the identification ambiguity of entity boundaries in event elements in the current study of internet hot events extraction. In this paper, we explore the joint extraction of events using sequence annotation, and construct a joint extracting method based on BERT-CNN-CRF model to obtain the input text fusion full-text semantic information vector by using BERT, and extract local contextual semantic features by convolutional neural network (CNN), and obtain joint extraction results by combining conditional random fields (CRF). After crawling several large public social media platforms with over ten thousand of data and comparing three baseline approaches in the latest event extraction methods, our proposed joint BERT-CNN-CRF extraction model has higher accuracy and better efficiency.
A Safe Architecture of 5G Network Intelligence Based on Federated Learning and NWDAF

Abstract: Intelligent communication network is the technical development trend of 5G and post-5G era. Big data analysis is the foundation of intelligent network while data isolation and privacy protection in network data analysis is a bottleneck problem. Federated learning is an emerging distributed machine learning framework which can make use of all parties' data for joint modeling while protecting the data privacy. In this paper, we propose an intelligent communication network framework which combines 5G Network Data Analysis Function (NWDAF) and federated learning to solve the above problem. Our work demonstrates that federated learning technology can ensure the data usage compliance in the process of 5G network intellectualization while solving the data isolation and data privacy protection problem.