Reviewerly/exHarmony huggingface dataset:
Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information

Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton’s cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models.
Real-Time Deep Learning-Based Anomaly Detection Approach for Multivariate Data Streams with Apache Flink

For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly detection algorithms have been widely proposed, there has been no work on how to perform these detection algorithms for multivariate data streams with a stream processing framework. To address this issue, we present a real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. We train a LSTM encoder-decoder model to reconstruct a multivariate input sequence and develop a detection algorithm that uses reconstruction error between the input sequence and the reconstructed sequence. We show that our anomaly detection algorithm can provide promising performance on a real-world dataset. Then, we develop a Flink program by implementing three operators which process and transform multivariate data streams in a specific order. The Flink program outputs anomaly detection results in real time, making system experts can easily receive notices of critical issues and resolve the issues by appropriate actions to maintain the health of the systems.
Classification Bullying Tweet Using Convolutional Neural Network with Word2vec

Twitter was a social media for interacting with friends, conveying thoughts, feelings, ideas, or just a medium for seeking entertainment or information. Twitter gave users the freedom to wrote anything there. That freedom may cause a lot of cyberbullying activities on twitter. One of the effects commonly felt by victims of bullying on social media was a feeling of insecurity that could even lead to depress. This studied classifies bullying tweets used the convolutional neural network method with the Word2vec model on Indonesian-language tweets. Word2vec pre-training provides better accuracy because word2vec considers the position and context of words in a sentence. The Convolutional Neural Network method was one of the deep learned algorithms that could provide more accurate and faster results by analyzing even large amounts of data. The data testing process had an accuracy valued of 93,83&#x0025;. The Convolutional Neural Network model used in data testing was done by combining the max pooling results from bigram, trigram, and fourgram, because it could got smaller and more complex features also the amount of information extracted became more by used a learned rate of 0,001 and a dropout of 0,4.
Two useful Python tools - dimpy and tablefile for data analysis applications

Abstract Python’s list array is more powerful than arrays in other languages like C, C++, Fortran, or Java. However, in some cases it becomes tedious and complicated to construct a multidimensional ‘list’ type array in Python. A Python tool namely ‘ dimpy ’ is discussed in this paper which can easily generate any multidimensional ‘list’ type array in python. Another Python package called tablefile for reading and analysing column-wise data from a data-file is also discussed. How these two tools may be useful and reduce steps of programming is shown by using some mathematics and physics related problems.
Social Media Usage and Business Performance among Small and Medium Scale Marketers in Batticaloa, Sri-Lanka

The popularity of social media has exploded in decade ago, and during this pandemic situation, the need and the role of social media plays a massive role. The current study intended to produce empirical evidence on the relationship and the effect between social media usage and business performance in the context of Small and Medium Enterprises in Batticaloa. Mainly the research aims to reveal the influence of customer relationship management, market access, and information on the advancement of SMEs. The study utilized a descriptive survey methodology, with 250 respondents. The data were analyzed using descriptive, univariate, and bivariate analysis. The study revealed that social media has a strong positive impact on the business performance of SMEs. This study postulated an awareness of the significance of social media and its benefits. The results would motivate and guide SMEs in the espousal of social media as a tool for business activities.
Practical security analysis of continuous-variable quantum key distribution with an unbalanced heterodyne detector

When developing a practical continuous-variable quantum key distribution (CVQKD), the detector is necessary at the receiver’s side. We investigate the practical security of the CVQKD system with an unbalanced heterodyne detector. The results show that unbalanced heterodyne detector introduces extra excess noise into the system and decreases the lower bound of the secret key rate without awareness of the legitimate communicators, which leaves loopholes for Eve to attack the system. In addition, we find that the secret key rate decreases more severely with the increase in the degree of imbalance and the excess noise induced by the imbalance is proportional to the intensity of the local oscillator (LO) under the same degree of imbalance. Finally, a countermeasure is proposed to resist these kinds of effects.
Machine learning method to solve the credit decision-making problem of small and medium-sized enterprises

Small and medium-sized enterprises(SMEs) play important roles in our economy. However, the scale of SMEs is relatively small. There is also a lack of pledged assets, which results in a problem of difficulty in borrowing funds. Banks face the problem that how can they determine whether to lend or not when evaluating SMEs credit risk factors, including the strength and reputation, as well as credit strategies, such as loan quota, interest rate and term. In this paper, support vector machine and decision tree method are comprehensively used to learn the enterprises data and evaluate the credit rating of enterprises lacking credit information. A linear optimization model is established based on the bank's principle of maximizing the expected annual profit, and this paper provides the optimal strategy for banks to decide the amount of loans granted to each enterprise. In addition, emergency situation is taken as an example, such as Covid-19 epidemic, by utilizing machine learning method and optimization theory, based on the fact that banks expect to maximize profit, establish an optimization model with wider applicability. And this paper provides credit strategy for banks when facing unexpected environmental emergency.
Engineering Financial Performance Evaluation of Wireless Network Based on Intelligent Neural Network Model

Under the global economy, enterprises in the financial industry are facing plenty of opportunities and severe challenges. Aimed at providing a reference enterprise performance evaluation system for related enterprises, the proposed model helps enterprises to learn and sort out their own performance evaluation system according to this structure. A prediction model of BP neural network (BPNN) based on the wireless network is studied as the performance data prediction algorithm. Firstly, the feasibility of this algorithm is analysed through prediction training. Secondly, the proposed neural network algorithm is compared with the traditional algorithm for data prediction. It turns out that this neural network prediction algorithm based on wireless communication is not only universal to the prediction data but also superior to the traditional prediction algorithm in both error gap and relative average error compared with other traditional algorithms. On this basis, the particle swarm optimization (PSO) algorithm is also used to evaluate the performance indicators of three enterprises, and accurate numerical values are obtained to express the corresponding results. Therefore, it is concluded that the subalgorithm can be applied to the enterprise performance evaluation team in the financial industry.
Endorsement Policy for Industrial Internet of Thing with Private Blockchain

In the industrial Internet of Things (IIoT) environment, managing a significant amount of real-time sensing data generated by IIoT devices is a primary concern. Edge computing technology can effectively resolve this problem for low latency data exchange. However, it faces security challenges and management overhead of large volume of real-time IIoT traffic. In this paper, we propose to integrate a private blockchain into edge computing server systems to tack the challenges. The proposed system overcomes security challenges by allowing IIoT edge devices to execute smart contracts and verify transactions in private blockchain. In order to alleviate management overhead, we simplify the existing transaction process by introducing a novel endorsement policy. Performance evaluation shows that the proposed system can satisfy the low latency requirement and reduce energy consumption of the IIoT devices.
Mobile Augmented Reality Application for Component Identification

Abstract This article discusses the development of augmented reality (AR) based object identification applications. The purpose of this study is to improve the distribution station component identification workbook. Through the implementation of the developed application, it is assumed that it will be easier to understand the functionality of the components of the distributing station. The augmented reality application was developed for the android platform. The result showed the application of Distributing Station-AR has been developed by applying a marker-based tracking method. The application test has successfully displayed the three-dimension components of distributing station using a marker on the component identification workbook of distributing station. Product performance is known to be very good and product validation is also very good, including material validation, media validation, and first user responses. For further development, this application is expected to add animation of the working principle of AR-based components.
Variable Selection for Bayesian Decision Tree Ensembles

We consider nonparametric variable selection using Bayesian decision tree ensembles, primarily from a fully-Bayesian perspective. The variable selection problem for Bayesian decision trees seems, on the surface, to be very simple --- if a variable has been used as a branch in a decision tree, then it is in the model, and otherwise it is not. We show that the situation is more subtle than this, with poorly chosen priors resulting in overly-dense models. We review methods that do not use the model itself to determine variable importance and fully-Bayes approaches which derive model inclusion probabilities from the model itself. Fully-Bayesian approaches rely on sparsity-inducing priors to perform well; such priors include the Dirichlet prior, Spike-and-Forest priors, and a new class of Gibbs priors which we introduce. We illustrate these approaches on simulated and benchmark datasets. We also discuss extensions of these approaches when there exists additional structure, such as graphical or grouping structure, on the predictors.
MAPE accuracy of CPO Forecasting by Applying Fuzzy Time Series

Accuracy in each forecasting is needed in order to find out how much error will occur from the forecasting results. The forecasting process is included in the data mining process, which is to convert data into new knowledge. Forecasting is also widely used by commodity business players such as CPO (Crude Palm Oil). CPO is a staple of human life so there is a need for a technique to increase the business. one of the things that can be forecasted from CPO is the price of CPO. By using the times series, it is possible to predict CPO prices. The accuracy measurement that can be used is MAPE (Mean Absolute Percentage Error) for forecasting. In the test, the time series data used is the average monthly yield of CPO prices starting from December 2010 to April 2021 as many as 125 data. In this article, we tested 60% of the training data and 40% of the test data, and the MAPE obtained was 0.03463929%, and the MAPE obtained with 80% of the training data and 20% of the test data was 0.04392103%. The result of MAPE does not seem that much different so that it can be tested in various other accuracy techniques
Türkiye’deki Çeviri Konulu Tezler Üzerine Bibliyometrik Bir İnceleme (1985-2020)

under this study.This search has yielded a total of 814 theses and dissertations, including
Application of Hybrid GA-SOFM Neural Network in Quality Evaluation of English Teaching

SOFM neural network algorithm adopts an unsupervised clustering algorithm, which can map the cluster center generated after calculation to a surface or plane, which makes the topology of the network have high stability. The GA algorithm completes the operation process through three operators: selection, crossover, and mutation. It has good global optimization and robustness. In this paper, the SOFM algorithm is improved by a GA algorithm and a hybrid GA- SOFM neural network algorithm is established. The algorithm is applied to the quality evaluation system. According to the results of the MATLAB simulation experiment, the evaluation accuracy and absolute error are determined and compared with the previous optimal GA-RBF hybrid algorithm model. The results show that the average evaluation accuracy of the proposed algorithm model evaluation is 89.43%, and its absolute error is 0.017. It shows that the quality evaluation model based on a hybrid GA-SOFM neural network can effectively and accurately evaluate quality.
Adaptation to delayed visual feedback of the body movement extends multisensory peripersonal space

The space surrounding the body in which individuals interact with the environment is known as the peripersonal space (PPS). Previous studies have reported that PPS has multisensory nature. However, the relationship between the multisensory nature of PPS and an individuals' defensive actions has not been fully clarified to date. We investigated this relationship by examining the multisensory representation of PPS under situations in which visual feedback of body movements was delayed by using a virtual reality system. The results indicated that body-movement delays extended the multisensory PPS, suggesting that body-movement delays increased the potential threat of distant objects because it was necessary to prepare defensive actions sooner. The previous findings can be interpreted that PPS is modulated by the spatio-temporal relationship between people and external stimuli. This view may provide evidence of interactions between defensive and nondefensive functions of the multisensory PPS.
The Application of the Accounting for the Online Store

This study aims to determine the cash sales accounting information system that is suitable for online store applications. Using qualitative methods with the object of research, namely the online stores. Sources of data obtained from several online stores. Data collection methods used are semi-structured interviews, observation, and document analysis. From the analysis and design of the buying and selling cycle information system in online business development, it was found that the development of information systems is a very important stage in the efficiency and effectiveness of the buying and selling cycle. By implementing a good and correct accounting information system design on an online store, it can facilitate the recording and minimize the occurrence of errors/loss of data. In addition, with a good information system design, the division of tasks between various sections becomes clear.
AGNNGRU-CDR: Attentive Graph Neural Networks and Gate Recurrent Unit for Cross-Domain Recommendation

Cross-domain recommendation is a hot research field of recommendation systems because it can alleviate the problem of data sparsity. Recent work focus on integrating user long-term and short-term preferences in cross-domain recommendation. These methods learn long-term preferences from user attributes and short-term preferences from attributes of recent interactive items. But these methods only use short-term interactions and attribute information, ignoring the long-term interactions. Therefore, how to make full use of interactive items to achieve accurate recommendation in cross-domain recommendation is the main issue of our research. To this end, this paper proposes a model based on Attentive Graph Neural Networks and Gate Recurrent Unit, namely, AGNNGRU-CDR. The main work is as follows: (1) Learning long-term preferences by gated graph neural networks from long-term interactions and learning short-term preferences by gate recurrent unit from short-term interactions. (2) Concatenating and transferring user preferences through attention mechanism. Experiments on public datasets demonstrate the superiority of the method proposed in this paper.
Remote sensing image uneven haze removal based on correction of saturation map

Remote sensing images are frequently affected by the random haze. An efficient method to remove uneven haze is proposed based on dark channel prior (DCP) corrected by saturation map and atmospheric haze model with consideration of multiple atmospheric scattering effects. First, the non-negligible atmospheric multiple scattering phenomenon is simulated by atmosphere point spread function, and the atmospheric multiple scattering haze model is established. Second, an effect method is proposed to calculate the saturation map to correct the DCP, which is used to eliminate the intensity residual of remote sensing images and re-establish the transmission calculation formula. Finally, the image is entered into the integrated restoration framework to remove the haze. Due to the correction of the saturation map, this method can effectively remove the inhomogeneous haze and retain detailed information in the haze-free areas. Compared with other excellent dehaze approaches, qualitative and quantitative experiments are carried out to demonstrate that the proposed method can effectively recover scenes in hazy regions and can retain more detailed information in haze-free regions.
Development of Early Childhood Education Management Model Based on National Education Standards and Management Information System (MPAUD-SNSI)

The low quality of early childhood education management in Indonesia can be seen from the results of accreditation that refer to national education standards. This study offers a solution that is to streamline management through the application of a management model based on national standards for early childhood education, which is integrated with a management information system. This article describes the process of formulating and testing a model called MPAUD-SNSI. The tests conducted at 45 schools in Bengkulu Province showed that the model and application of the management information system produced in this study were valid and reliable. The spider diagram simulation feature in the application can conduct a self-evaluation before applying for accreditation. This evaluation helps schools to improve the quality of management sustainably. The empirical test results show that if the indicators in the PAUD-SNSI model are applied in schools, the level of effectiveness of school management can reach the very effective criteria.
Rozmyte mapy kognitywne jako technika prowadzenia ustrukturyzowanej analizy systemowej w obszarze nauk o bezpieczeństwie

Fuzzy Cognitive Maps as a Technique for Conducting Structured Systems Analysis in the Area of Security Sciences The article aims to present and introduce the main assumptions and procedures for creating fuzzy cognitive maps (FCM) in the field of security studies. FCM are a tool for conducting a systematic analysis of phenomena with a complex structure and consisting of many interrelated elements. The text was divided into three main parts devoted accordingly to theoretical premises of the FCM (the system analysis, mental and cognitive maps), the FCM itself, and the procedure for creating and using RMK for analytical purposes.
Nonclassicality of photon–modulated spin coherent states in the Holstein-Primakoff realization

Two new photon-modulated spin coherent states (SCSs) are introduced by operating the spin ladder operators J ± on the ordinary SCS in the Holstein–Primakoff realization and the nonclassicality is exhibited via their photon number distribution, second-order correlation function, photocount distribution and negativity of Wigner distribution. Analytical results show that the photocount distribution is a Bernoulli distribution and the Wigner functions are only associated with two-variable Hermite polynomials. Compared with the ordinary SCS, the photon-modulated SCSs exhibit more stronger nonclassicality in certain regions of the photon modulated number k and spin number j , which means that the nonclassicality can be enhanced by selecting suitable parameters.
Telecommunication Using 3DCG Avatars Manipulated with Finger Plays and Hand Shadow

The authors propose a manipulation method of 3D avatars in telecommunication. The idea of the method came from finger plays and hand shadow. The avatar animates synchronously with the user’s finger play or hand shadow. The users can manipulate their avatars with the hands easily and intuitively. The authors implemented a telecommunication system and carried out an experiment. The participants were able to communicate with manipulating their avatar well. They felt the communication using the system comfortable and familiar.