Reviewerly/exHarmony huggingface dataset:
Proof-of-Improved-Participation: A New Consensus Protocol for Blockchain Technology

The Internet of Things (IoT) is converting today's physical world into a complex and sophisticated network of connected devices on an enormous scale.The existing malicious node detection mechanism in traditional approaches lacks in transparency, availability, or traceability of the detection phase.To overcome these concerns, we provide a decentralized technique using blockchain technology.Despite the fact that blockchain technology is applicable to create that type of models, existing harmony set of instructions are susceptible to do violence to such as DoS and Sybil, making blockchain systems unfeasible.Here, a new Proof-of-Improved-Participation (PoIP) harmony instruction was suggested that benefits the participation rules to select honest peers for mining while limiting malicious peers.Under an evaluation the PoIP outperforms the Proof-of-Work (PoW) instructions are demonstrated, Proof of Stake (PoS) instructions in terms of energy consumption, accuracy, and bandwidth.To compare the three consensus protocols with respect to efficiency, we build a lightweight mining model and find that PoIP consensus has greater efficiency than PoW and PoS.PoIP has 25% lower attack risk than existing consensus.As a consequence, our suggested methodology can provide the needed security with minimal attack risk and high accuracy, according to the analysis results.As a result, suggested consensus is more efficient than existing methods in terms of block generation time.Hence we suggest that suggested consensus is very suitable for IoT-based applications especially in healthcare.
Exploring the associative learning capabilities of the segmented attractor network for lifelong learning

This work explores the process of adapting the segmented attractor network to a lifelong learning setting. Taking inspirations from Hopfield networks and content-addressable memory, the segmented attractor network is a powerful tool for associative memory applications. The network's performance as an associative memory is analyzed using multiple metrics. In addition to the network's general hit rate, its capability to recall unique memories and their frequency is also evaluated with respect to time. Finally, additional learning techniques are implemented to enhance the network's recall capacity in the application of lifelong learning. These learning techniques are based on human cognitive functions such as memory consolidation, prediction, and forgetting.
Cloud Computing Resource Prediction Model Based on Time Convolutional Network

With the continuous progress and development of modern science and technology, the research on cloud computing-related fields is constantly conducting more in-depth exploration. During the real-life use of cloud computing operations, as the number of tenants continues to increase, the resource usage load capacity of the relevant platform has also undergone tremendous changes. In order to enable the tenants to complete higher-level optimization of the relevant performance and indicators during the actual work of the platform, this article explains how to perform related network resource models on the premise of cloud computing operation management. The researchers used this cloud computing network operation forecasting system as the basic point of view for experimental research and explained and summarized all relevant research results and specific instructions on multivariable load sequences in detail. The multivariable load sequence is embedded in the dimension of the phase space in the calculation process, and the generalization operation from single variable to multivariable can be carried out. However, every time the expansion calculation is performed, the selection criteria available for the researcher to calculate will be reduced, making the result a reconstructed phase space with uncertainty. Therefore, in order to ensure the accuracy of the data of the research experiment results, the researchers must simplify the model, focus on further discussing the correlation between multivariable load sequences in the mechanism, and reduce the number of data calculations in the process. The redundant information generated can select more reasonable data information as input variables in the research process.
Application of Personal Information Privacy Protection Based on Machine Learning Algorithm

Based on a machine learning algorithm, this paper deeply explores the privacy protection of personal information. In this paper, the definition of the machine learning algorithm is put forward, the design idea of privacy protection in joint machine learning platform is studied, and the architecture model and model parameter updating strategy of joint machine learning under privacy protection are designed. To protect the privacy of personal information, this paper also proposes a data homomorphic verification mechanism to prevent the global parameters from being tampered with by malicious cloud servers. In order to verify the performance of the models constructed in this paper, the comparative experiments of different models are carried out. The experimental results show that this algorithm has a fast convergence speed, and the average error rate decreases by 4.17% compared with the traditional algorithm. Moreover, the accuracy of this algorithm reaches 95.37%, which is about 8.76% higher than the previous algorithm. This model is applied to the field of personal information privacy protection, which can provide a safe and reliable environment for personal information privacy and effectively protect the privacy of data owners. And, means and reference value is provided for the development direction of privacy protection.
CR-BA: Public Key Infrastructure Certificate Revocation Scheme Based on Blockchain and Accumulator

With the development of blockchain, many studies apply blockchain to certificate revocation. However, existing blockchain-based certificate revocation schemes have two shortcomings. First, the storage overhead on the blockchain is relatively large. Second, as the number of revoked certificates increases, the misjudgment rate of certificate status will increase accordingly, so a public key infrastructure implementation certificate revocation scheme based on blockchain and accumulators, called CR-BA, is proposed. First, CR-BA expands the certificate structure, adding a revocation factor and a smart contract account for accessing the blockchain in the certificate extension, which is filled by the CA when the certificate is generated. Then, when the certificate is to be revoked, CA generates the revocation fingerprint through the revocation factor and publishes it to the blockchain. Finally, when the user needs to verify the status of the certificate, CA calculates the revocation fingerprint according to the revocation factor on the certificate, then compares it with the existing revocation fingerprint on the blockchain, and returns the comparison result to the user. The experimental results show that this scheme can effectively overcome the storage and misjudgment problems caused by existing blockchain-based certificate revocation schemes and improve the query efficiency of certificate revocation information.
Multimedia Drama Imaging Technology Based on Big Data Information System

Opera is a unique comprehensive stage art form in China. As a traditional Chinese drama style, it is known as the three ancient drama cultures in the world together with the tragicomedy of ancient Greece and the Sanskrit drama of India. However, in the trend of the new era, Chinese opera is facing a severe existential crisis. How to innovate the opera and make people fall in love with the opera again is a problem worthy of research and analysis at present. This study takes the research of multimedia opera image technology as the object, aims to improve the perception and experience of opera, and studies how to use modern technology to improve opera image. First, this study briefly expounds on the presentation methods and current situation of traditional opera video technology. This study then describes in detail the imaging technology that combines opera and modern technology and then establishes the algorithm model of virtual reality imaging technology. And it then describes in detail the video technology that combines Chinese opera with multimedia technologies such as projection and virtual reality interaction. It then established a virtual reality technology algorithm model for opera images. This study then experiments, analyzes, and improves the multimedia opera video technology based on virtual reality. Finally, by means of a questionnaire survey, it investigates and analyzes the viewing attitude of opera images based on virtual reality technology. Through the research of virtual reality image technology and the investigation of citizens, this study obtains the methods and models to improve traditional opera images and effectively innovates opera. In a survey of citizens, it was found that nearly 70% of the respondents held a positive and optimistic attitude toward the virtual reality-based opera video technology, indicating that people are willing to accept the combination of traditional culture and modern technology. In the element loss detection experiment, it was found that nearly 70% of the interviewees were very interested in the method of combining traditional opera with modern technology. The model of virtual opera scene construction was improved so that the element loss rate was lower than 4%. The element representation delay is less than 3%, which improves the smoothness of the picture and maintains a good viewing experience.
The Development of an Android-Based “LaporKPS” Application to Support the Service Center for Reports of Sexual Violence and Harassment Cases

Cases of sexual violence and harassment have recently occurred among the public. Many print and non-print media report on issues of sexual violence and harassment. The PPP case is an abuse of the relationship between women and men which harms one of the parties because they are harassed or degraded in dignity, either verbally or non-verbally. Semarang was the highest rank in cases of sexual violence in Central Java in 2018. The PPP case reporting mechanism requires victims to go to a service center. Many risks make victims reluctant to report the incident that occurred because they are embarrassed in society and afraid that perpetrators will intimidate them. This paper aims to develop an android-based application to summarize the reporting mechanism by reporting PPP incidents experienced by victims using gadgets. Several menus provided include reporting menu, counseling menu, article menu, and Robo menu to identify categories of forms of harassment based on user input. The application is developed by using a customized waterfall method. Several testing has been developed to evaluate the application, such as Blackbox testing (achieves 100%), usability testing (achieves 79%), and media content testing (achieves 85.45%).
Real-time Multi-person Pose Tracking Method Using Deep Reinforcement Learning

To address the problem of low tracking accuracy caused by many recognized objects in the existing methods, we propose a real-time multi-person pose tracking method using deep reinforcement learning. First, the convolutional neural network (CNN) is used to predict the human key points and center vector in grid mode, make the human key points point to the human center according to the center vector, group the human key points according to the distance from the human key points to the human center, complete the multi-person pose estimation, and obtain the human pose sequence diagram. Then, the human pose sequence diagram is input into the deep reinforcement learning network, and the pose label and category label are output by the supervised learning and training stage. The best pose tracking strategy obtained in the reinforcement learning and training stage is applied to online tracking. Finally, CNN is used to predict the rectangular frame position of the pose instead of the target pose, and the tracking is completed when the pose stops. At this time, the rectangular frame position is the result of multi-person pose tracking. The results show that the maximum expected average overlap (EAO) of the proposed method is 0.53. When the root mean square error (RMSE) of the position component threshold reaches 8, the accuracy has been stable at 0.98%. Therefore, the proposed method has high tracking accuracy. In the future, it can be applied to smart home scenarios to realize smart home human pose tracking, effectively identify human dangerous pose and ensure residents’ life safety.
Papyri: better documentation for the scientific ecosystem in Jupyter

We present here the idea behind Papyri, a framework we are developing to provide a better documentation experience for the scientific ecosystem.In particular, we wish to provide a documentation browser (from within Jupyter or other IDEs and Python editors) that gives a unified experience, cross library navigation search and indexing.By decoupling documentation generation from rendering we hope this can help address some of the documentation accessibility concerns, and allow customisation based on users' preferences.
Self-Guided Belief Propagation – a Homotopy Continuation Method

Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP) that incorporates the pairwise potentials only gradually. This homotopy continuation method converges to a unique solution and increases the accuracy without increasing the computational burden. We provide a formal analysis to demonstrate that SBP finds the global optimum of the Bethe approximation for attractive models where all variables favor the same state. Moreover, we apply SBP to various graphs with random potentials and empirically show that: (i) SBP is superior in terms of accuracy whenever BP converges, and (ii) SBP obtains a unique, stable, and accurate solution whenever BP does not converge.
A Semantic Web-Enabled Approach for Dependency Management

The use of external libraries in today’s software projects allows developers to take advantage of features provided by such application programming interfaces (APIs) without having to reinvent the wheel. However, APIs have also introduced new challenges to the software engineering community (e.g. API incompatibilities, software vulnerabilities, and license violations) that extend beyond traditional project boundaries and often involve different software artifacts. One potential solution to these challenges is to provide a technology-independent representation of software dependency semantics and its integration with knowledge from other software artifacts. In our research, we take advantage of the semantic web (SW) and its technology stack to establish a unified knowledge representation of build and dependency repositories. Given this knowledge base, we can now extend and integrate other (heterogeneous) resources to allow for a flexible and comprehensive global impact analysis approach. To illustrate the applicability of our SW-enabled modeling approach, we discuss two different applications. These applications illustrate how our modeling approach can not only integrate and reuse knowledge from dependency management systems and other software artifacts, but also take advantage of inference services provided by the SW to support novel software analytics services across artifact and project boundaries.
Research on the Semantic Analysis Method of Translation Corpus Based on Natural Language Processing

Accurate recognition and analysis of semantics is the most important research field in the process of English translation with the help of natural language processing technology. This paper proposes an English semantic analysis method based on the neural network. First, the idea of model transfer is used to construct a topic segmentation model and the topic granularity segmentation of the translated text is carried out. Then, in order to obtain all the information in the English text, the recursive neural network is selected to recognize the word model. In order to recognize English texts with different sentence patterns, the long-term and short-term memory network is selected to extract the useful information of the text. Through the experimental data measurement and analysis results, compared with the traditional sentence analysis methods, the accuracy of the proposed method is as high as 95.8% and the model occupies less hardware resources.
Tourism Consumer Demand Forecasting under the Background of Big Data

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.
Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint

In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.
An Analysis of Cooperative Principle Maxim in the Written and Spoken Mode of Communication between Teacher and Students during Online Learning

This study had analyzed the application of the Cooperative Principles maxim in the teacher and students� interaction during online learning, either in the spoken or written mode of communication. Previous research argues that there are some differences in interaction through spoken and written modes of communication. This current research analyzed the differences between the application of Cooperative principles maxim in the spoken and written mode of communication, it is because most of the research about Cooperative principles maxim only analyzed the utterances in one mode of communication. Then, although there is research on the Cooperative Principle maxim in both modes of communication still not been found that compared the Cooperative Principle maxim in the context of teacher and student interaction in that two modes of communication. This current research employed the descriptive qualitative method. The vocational high school students become the participants of this study because there is a must for them to be fluent in English because of the need to work after they graduate. The researcher is the primary instrument. However, the researcher also used a secondary instrument to find results. The secondary tool is the table used by some previous research on analyzing the flouting and violating of the maxim. Further, the results show in the teacher and students' interaction through the spoken mode of communication, that the most maxim flouted by the teacher and students is the maxim of quantity. Then, the most maxim violated in the spoken mode of communication by the teacher is the maxim of quantity however, students violated the maxim of quality. In the written mode of communication, teachers flouted the maxim quantity and manner for the most, but students flouted the maxim of quality and manner. However, the researcher did not find the occurrence of a violation of the maxim in the written mode of communication.
Fairness-Aware Predictive Graph Learning in Social Networks

Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data.