Reviewerly/exHarmony huggingface dataset:
Data Mining-Based Metrics for the Systematic Evaluation of Software Project Management Methodologies

Metrics allow the evaluation of project management methodologies. They can identify aspects related to the quality of the results and the economy of resources. This chapter introduces a proposal for metrics to assess the performance in the use of project management methodologies and the first results on a small number of use cases. From bibliographic sources, a set of new metrics are defined to include those evaluations that up to now are subjective or nonexistent. As part of the scope, a detailed presentation of some of the metrics and their application to a case study, along with some statistics, are included. The applicability of the proposal to real cases is also analyzed. The full derivation of the metrics, the complete listing, and the functional prototype that is being used to apply them are not part of the scope.
Prediction of The Export Value in Indonesia for The Year 2021 as The Impact of Covid-19 Pandemic Using ARIMA Algorithm

The condition of the Covid-19 Pandemic has greatly impacted the Indonesian economy, especially the export sector, so that cooperation between countries was temporarily suspended due to the government’s policy regarding the lockdown. Therefore, this research was conducted to predict Indonesian export value in 2021 as a result of the pandemic situation. The prediction method for processing export index value in this research uses a time series algorithm ARIMA. The processing of this time series data uses RapidMiner tools with the CRISP-DM data mining method. The accuracy of the prediction results is obtained by comparing the prediction results using RapidMiner tools with the results of the analysis of the world bank’s forecasts regarding the export sector in Indonesia. Predictive data that has been obtained will be compared for the years 2019, 2020, and 2021.
Study on Face Recognition Techniques

Face recognition is a favoured research subject as it's a more conventional method to record students' attendance, an identification method by using individuals' faces using images, or (real-time) camera. In these papers, the authors have researched and discussed the different techniques used to be implemented in face detection, face recognition, and face recognition through their methodology and algorithm's accuracy. The purpose of this research is to explore the different algorithms implemented in creating a face recognition system with its accuracy performing the system's algorithm. The authors have identified 30 different research papers, conference papers, and journal papers within the period of the years 2012–2020. Each paper discussed and achieved results based on the accuracy of the algorithm used, demonstrating its advantages and disadvantages. Thus, giving the conclusion which algorithm is suitable to be used in a face recognition system.
Laser scanning-based method for identifying damage location of civil construction node structure

The traditional structural damage location identification method has the problem of large error in damage location ranging and damage area calibration. Therefore, this paper proposes a damage location identification method for civil building node structure based on laser scanning. According to the civil construction node structure, choose the right means of single line scan, calibrated laser scanner to scan parameters, construction node structure point cloud data, build the nonuniform grid, streamlining the point cloud data, filter for removing noise point cloud, the extraction and matching feature point cloud data, building structure model, determine the node structure damage location coordinates. Experimental results show that the design method reduces the damage location error and damage area calibration error, and improves the damage location identification accuracy.
Image Analytics: A consolidation of visual feature extraction methods

Revolutionary advances in machine and deep learning techniques within the field of computer field have dramatically expanded our opportunities to decipher the merits of digital imagery in the business world. Although extant literature on computer vision has yielded a myriad of approaches for extracting core attributes from images, the esotericism of the advocated techniques hinders scholars from delving into the role of visual rhetoric in driving business performance. Consequently, this tutorial aims to consolidate resources for extracting visual features via conventional machine and/or deep learning techniques. We describe resources and techniques based on three visual feature extraction methods, namely calculation-, recognition-, and simulation-based. Additionally, we offer practical examples to illustrate how image features can be accessed via open-sourced python packages such as OpenCV and TensorFlow.
Research on Cloud Economy Management Model Based on Resource Scheduling Algorithm

Cloud Computing is considered as the most important form in the future development of Internet services. However, the current security of cloud computing is facing serious problems. At present, in order to put forward a reasonable solution to cloud security, it is a hot topic of operation computing. Trust is an important security choice in distributed Internet environment. In this paper, the trust mechanism is introduced to solve the security problems in distributed, heterogeneous and dynamic cloud computing environments.
Better than Bitcoin? Can cryptocurrencies beat inflation?

Unlike Central banks, cryptocurrencies can easily adjust their inflation rate with simple changes to code. Bitcoin, with a finite eventual supply of tokens, is increasingly gaining acceptance as an alternative long-term digital store of value with similar anti-inflationary characteristics to gold. However, recent innovations on the Ethereum blockchain have shown that it is possible for cryptocurrencies to become deflationary ¬– specifically through the destruction of transaction fees. With up to half of the networks blocks destroying more Ethereum than is created, the notion that Bitcoin offers the best inflationary hedge among the cryptocurrencies is increasingly coming under threat. Such protocol improvements could pave the way for more cryptocurrencies to improve their anti-inflationary characteristics, becoming better stores of value than Bitcoin.
A Parallelization Algorithm of Singular Value Decomposition

Abstract Matrix factorization algorithm is one of the recommendable algorithms. In order to tackle the inefficiencies of the traditional matrix factorization algorithm like the slow training time and the insufficient computing resource for the mass data, a parallelization algorithm of singular value decomposition (SVD) under the Spark framework is proposed to perform SVD, standardization, and dimensionality reduction for the user-rating matrix, and obtain the user-feature matrix and project-feature matrix. The recommendation model is obtained by determining the prediction rating. MovieLens data show that this algorithm can significantly shorten the training time of the model, improve the running efficiency of the recommendation algorithms for the mass data, and improve the algorithm accuracy.
An Efficient Real-Time Digital Channelization Method Based On Frequency-Domain Windowing

In order to reduce the influences of spectrum leakage and scalloping loss of fast Fourier transform channelizer on the performance of digital receivers, an efficient real-time digital channelization method based on frequency-domain windowing was proposed. This method utilized a special window on the basis of windowed fast Fourier transform model. Theoretical derivation revealed that the proposed method could be realized only by adding an adder to each channel calculation, which facilitated the implementation in FPGAs. Moreover, the sidelobe is reduced by 10dB and the gain of channel intersection is increased by 2dB compared with fast Fourier transform channelizer. The simulation results showed that the proposed method had improved the signal-to-noise ratio, the accuracy of amplitude calculation, and the instantaneous dynamic range of the receivers.
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV—A Practical Approach to Binary Classification Problems

We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statistical programming language R. To illustrate the methods applied, we supply a simulated database of 10,000 glioblastoma patients who underwent microsurgery, and predict the occurrence of 12-month survival. We walk the reader through each step, including import, checking, and splitting of datasets. In terms of pre-processing, we focus on how to practically implement imputation using a k-nearest neighbor algorithm, and how to perform feature selection using recursive feature elimination. When it comes to training models, we apply the theory discussed in Parts I-III. We show how to implement bootstrapping and to evaluate and select models based on out-of-sample error. Specifically for classification, we discuss how to counteract class imbalance by using upsampling techniques. We discuss how the reporting of a minimum of accuracy, area under the curve (AUC), sensitivity, and specificity for discrimination, as well as slope and intercept for calibration-if possible alongside a calibration plot-is paramount. Finally, we explain how to arrive at a measure of variable importance using a universal, AUC-based method. We provide the full, structured code, as well as the complete glioblastoma survival database for the readers to download and execute in parallel to this section.
Visibility detection in foggy weather based on complex network

Aiming at the visibility prediction problem that is currently concerned in the transportation and aviation fields, an effective detection method based on complex network theory is proposed. Firstly, preprocess the images in the FROSI data set to obtain the gray image after the background difference subtraction, and derive and calculate the transmittance. Secondly, a corresponding mathematical model based on a complex network is proposed. After three steps of gray contour extraction, recognition parameter extraction, and image recognition and classification, a simulation platform is built and canny operator is used to complete image edge feature contour extraction. Finally, the processed image is combined with the formula to detect the visibility change trend in different time periods and compare it with the true value for comparison, the corresponding evaluation results are obtained from the relative error analysis.
A Hat-shaped Pressure-Sensitive Multi-Touch Interface for Virtual Reality

We developed a hat-shaped touch interface for virtual reality viewpoint control. The hat is made of conductive fabric and thus is lightweight. The user can touch, drag, and push the surface, enabling three-dimensional viewpoint control.
The Development of Internet of Things Learning Kit to Enhancing Programming Skills for High School Students

The purpose of this research was the development of internet of things learning kit to enhancing programming skills for high school students. The research methodology is pre-experimental design. The research participants consisted of 9 experts in 3 domains – 3 experts in content in computer programming content, 3 experts in learning media designer, 3 experts in designing constructivist learning environments, and 68 students in high school of Demonstration School of Khon Kaen University Secondary School. The research instruments are the expert review recording for examining the quality in 3 domains as follows: learning contents, learning media, and instructional design and the questionnaire form to studying learner’s preference learning experience about the internet of things learning kit to enhancing programming skills. The results revealed that: The internet of things learning kit to enhancing programming skills for high school students were 8 elements as follows: 1) Problem base, 2) Resource, 3) Cognitive tools, 4) Collaboration, 5) Programming Problem Solving Center, 6) Scaffolding, 7) Programmer Community, and 8) Programming Coaching Room. The result of experts’ reviews the efficiency of internet of things learning kit to enhancing programming skills was 81.20% and the results of the learner’s preference about learning experience using the internet of things learning kit to enhancing programming skills was 75.96% with were higher than the specified criterion as 70%.
Bridging Upper Ontology and Modular Ontology Modeling: a Tool and Evaluation

Ontologies are increasingly used as schema for knowledge graphs in many application areas. As such, there are a variety of different approaches for their development. In this paper, we describe and evaluate UAO (for Upper Ontology Alignment Tool), which is an extension to CoModIDE, a graphical Protégé plugin for modular ontology modeling. UAO enables ontology engineers to combine modular ontology modeling with a more traditional ontology modeling approach based on upper ontologies. We posit – and our evaluation supports this claim – that the tool does indeed makes it easier to combine both approaches. Thus, UAO enables a best-of-both-worlds approach. The evaluation consists of a user study, and the results show that performing typical manual alignment modeling tasks is relatively easier with UAO than doing it with Protégé alone, in terms of the time required to complete the task and improving the correctness of the output. Additionally, our test subjects provided significantly higher ratings on the System Utilization Scale for UOA.
Distance learning – a possible form for the development of gifted and talented sudents

Distance / e-learning has real potential to stimulate the development of gifted and talented students. Even if it is not planned, organized and conducted in a format that allows them to manage learning that is relatively independent of the main flow ( main stream students), to enable students to individual control during work, methods for learning and organization of free time. The article raises the question of how distance learning, introduced in an emergency situation, has affected gifted and talented students. The answers are sought through the analysis of a survey conducted with teachers, gifted students and their parents. The different points of view lead to the conclusion that when developing a long – term strategy for working with talented students at school, the opinions and reactions of all participants in the learning process should be thoroughly studied.
Advances and progress of diffractive deep neural networks

In recent years, artificial intelligence has achieved unprecedented development, and deep learning, represented by neural networks, plays an important role. After the emergence of large-scale pre-trained models with trillions of parameters, the model performance is significantly improved while the burden of computational resources and energy consumption of hardware devices are also increased simultaneously, thus limiting its application in more practical scenarios. Compared with neural networks implemented based on electronic devices, those implemented based on optical devices are called optical neural networks, which have unique properties to overcome the dilemma above. One of the most representative works of optical neural networks these years is the diffractive deep neural network (D<sup>2</sup>NN). In this paper, the research progress of D<sup>2</sup>NNs is summarized in four aspects: basic theory, further analysis, improvement, and application. Besides, it is analyzed that the common defect of D<sup>2</sup>NNs from simulation to physical fabrication, and corresponding theoretical improvement method is also proposed. Meanwhile, to further reduce the impact due to the gap between simulation and physical implementation, and to enhance the robustness of the model, the D<sup>2</sup>NN training method based on generative adversarial network (GAN) is proposed. The D<sup>2</sup>NN combines optical transmission with deep learning to achieve complex pattern recognition tasks in the optical domain at the speed of light. It is believed that under continuous research, the D<sup>2</sup>NN can play a greater role in optical communications and other fields.
Learning A Linear Classifier by Transforming Feature Vectors for Few-shot Image Classification

Deep neural networks have achieved remarkable results in large-scale data domain. However, they have not performed well on few-shot image classification tasks. Here we propose a new meta-learning approach composed of an embedding network and a linear classifier learner. During the training phase, our approach (called Transformation Network) learns to learn a classifier by transforming the feature vectors produced by the embedding module. Once trained, a Transformation Network is able to classify images of new classes by the learned classifier. The ability of learning a discriminatively trained classifier could make our architecture adapt fast to new examples from unseen classes. We further describe implementation details upon the architecture convolutional networks and linear transformation operations. We demonstrate that our approach achieves improved performance on few-shot image classification tasks on two benchmarks and a self-made dataset.
Ability Oriented Modular Talent Training Plan – Taking Architecture as An Example

The training goal of application-oriented universities is to cultivate high-quality application-oriented talents with strong social adaptability and competitiveness. It is required that the cultivation of various majors should be closely combined with local characteristics and highlight the improvement of students’ application ability. Based on the above background, this paper puts forward the ability oriented modular talent training program, taking architecture as an example. Starting from the purpose of Application-oriented Colleges and universities, this paper uses the teaching achievements of colleges and universities to reform the existing curriculum structure system, analyzes and explains the problems existing in the current talent training mode of architecture major in Colleges and universities, and constructs a modular teaching system oriented by ability output, combined with the training mode of application-oriented talents, guided by professional ability, and adheres to the principle of integrating theory with practice Based on the principle of combining engineering certification, a complete modular architecture teaching system is constructed. In order to provide reference for the talent training mode of domestic application-oriented universities, it should formulate and explore the idea and talent training scheme of modular reform of Architecture Specialty in line with the orientation of the University.
Study on the Three Dimensional Evaluation Model of English Listening and Speaking in the Context of Big Data

With the all-round popularization of Internet technology, with the help of various mobile intelligent terminals, the era of big data has quietly come. As an important place to cultivate practical, applied and innovative foreign language talents, College English education is facing great opportunities and challenges. Based on this, this paper analyzes the current difficulties of College English audio-visual and oral teaching, and puts forward the multi-dimensional evaluation model of English audio-visual and oral under the background of big data.
Chinese Electronic Medical Record Named Entity Recognition based on FastBERT method

Chinese Electronic Medical Record Named Entity Recognition (CNER) is to identify and extract the entities related to medical and clinical practice from electronic medical records and classify them into pre-defined categories. In the past few years, deep learning methods have been applied to CNER and have achieved remarkable results, especially the BERT pre-training model. the BERT model can achieve good results, but the high model's training cost and slow inference speed are unbearable. In order to solve these problems, scholars use various methods to compress the BERT model, such as knowledge distillation and architecture adjustment. In this article, FastBERT is improved and applied to CNER. The sample adaptation mechanism of this model is used to pick up the inference speed. It is learned from experiments that this method can not only improve the reasoning speed of entity recognition, but also maintains good performance.
Interaction with UAV Formation based on Gesture Recognition with Leap Motion

UAV formation can combine different aircraft platforms and mission loads, and it also can give full play to the quantity and coordination advantages of formation. At present, a single UAV generally adopts human-computer interaction modes such as mouse, keyboard and sense of operation. When this interaction mode performs more complex combat tasks for UAV formations, its interaction performance and efficiency are often greatly restricted. In this paper, based on Leap Motion gesture recognition equipment, online recognition methods as well as operation and control strategies of various gesture categories for UAV formation operation and control are designed. Through classifying and redeveloping the recognizable gesture contents, control instructions for UAV formation are generated, which can be used to improve the human-computer interaction efficiency of UAV formation.
Unsupervised Skill Identification from Job Ads

Identifying skills in the huge number of job ads posted every day can give multiple entities the big picture of job market requirements. Such requirements will not only help universities adapt their curricula to produce more employable graduates but will also give meaningful insights for job seekers. Unfortunately, analyzing these ads is challenging because they are submitted in raw text and need to be structured in order to identify the required skills. Most prior work, however, focuses on detecting and exact matching skills in job ads through keyword matching from built-up skill bases. In this paper, we develop a new methodology for identifying skills in job ads. This methodology is two-fold. First, the methodology identifies skills from job ads expressed in sentences where we use sentence embeddings to detect skills in job ads. Secondly, technical words for software names and certifications are identified using Wikipedia. Experiments on a sample of real-world job ads show that the precision and recall yield promising results in mapping and extracting hard skills from job ads. Our findings can offer insights to universities and graduates about the top skills and competencies requested in the job market.
Interactions with Artificial Entities Reloaded: 20 Years of Research from a Social Psychological Perspective

Abstract Twenty years ago, we reflected on the potential of psychological research in the area of embodied conversational agents and systematized the variables that need to be considered in empirical studies. We gave an outlook on potential and necessary research by taking into account the independent variables behavior and appearance of the embodied agent, by referring to the dependent variables acceptance , efficiency and effects on behavior and summarizing moderating variables such as task and individual differences . Twenty years later, we now give an account on what has been found and how the field has developed – suggesting avenues for future research.