Reviewerly/exHarmony huggingface dataset:
Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature

It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic algorithms (GAs) and stochastic configuration networks (SCNs), and the SCN-based incinerator temperature model is obtained simultaneously. Firstly, filter feature selection is realized by calculating the MI value between each feature variable and the incinerator temperature from historical data. Secondly, the fitness function of GAs is defined by the root mean square error of the incinerator temperature obtained by training SCNs, and features obtained by MI methods are searched iteratively to complete the wrapper feature selection, where the SCN-based incinerator temperature prediction model is obtained. Finally, the proposed model is verified by MSW incinerator temperature historical data. The results show that the SCN-based prediction model using the hybrid selection method can better predict the change trend of incinerator temperature, which proves that the SCNs has great development potential in the field of prediction modeling.
Approach to Building a Model Complex for Managing the Development of Hydrogen Energy

Based on causal analysis of the trends in the development of hydrogen energy, the concept of the structure of a digital complex, which combines various mathematical and simulation models, is proposed. Development is the process of changing the structure of a system to achieve goals. We assume that such a complex will help identify bottlenecks in forecasting development and outline the necessary efforts to accelerate it.
Towards decentralized anthropological scholarship: Some ethical considerations

This article outlines the future of decentralized blockchain scholarship and some ethical questions we must consider in its wake. What is the role of research participants in a decentralized publishing ecosystem? And how do we move towards more just values and practices in intellectual creation and dissemination?
A Survey of Chinese Character Recognition Research Based on Deep Learning

Chinese character recognition has always attracted much attention in our country and is widely used in our lives and work. The combination of deep learning and neural networks and other algorithms has greatly improved the accuracy and speed. This article reviews the research status and related background of deep learning in the field of Chinese character recognition. First, it introduces the development process of Chinese character recognition and neural network algorithms. Secondly, it describes the Chinese character recognition architecture based on neural network, classifies and outlines the relevant methods of handwritten Chinese character recognition in simple scenarios and text recognition in complex scenarios, explains the construction and characteristics of network models, and analyzes and summarizes the characteristics of each network model. The shortcomings, and finally prospects for future research.
High-gradient pattern image velocimetry (HGPIV)

We present a new Image Velocimetry (IV) hybrid that estimates vector fields from images with widely different visualization pattern sizes such as those encountered in riverine bedform migration. The IV approach is obtained by complementing the Cross-correlation Method (CCM) with algorithms of Optical Flow Methods (OFM). The OFM procedures are first applied to automatically determine the optimal Search Windows (SWs) over the whole imaged area. Subsequently, the CCM utilized the established SWs to locally resolve velocity fields associated with the bedform movement. The new approach, labeled herein as High-Gradient Pattern IV (HGPIV), combines the advantages of both parent techniques to improve the accuracy and spatial resolution of the resultant global velocity field and significantly reduces the computational time. The HGPIV validation consists of comparing its results with those obtained with the CCM approaches applied for estimating the velocity field associated with bedform migration in a large river.
Trajectory Planning for Emergency Landing of VTOL Fixed-Wing Unmanned Aerial Vehicles

In recent years, inspired by technological progress and the outstanding performance of Unmanned Aerial Vehicles (UAVs) in several local wars, the UAV industry has witnessed explosive development, widely used in communication relay, logistics, surveying and mapping, patrol, surveillance, and other fields. Vertical Take-Off and Landing fixed-wing UAV has both the advantages of vertical take-off and landing of rotorcraft and the advantages of long endurance of fixed-wing UAV, which broadened its application field and is the most popular UAV at present. Recently, fixed-wing UAV failure analysis highlights that cruise engine shutdown is the most common reason for emergency landing, which is also a governing factor for Vertical Take-Off and Landing (VTOL) fixed-wing UAV failures. Nevertheless, the emergency landing trajectory of the latter UAV type after engine shutdown is different from that of the conventional fixed-wing UAVs due to the VTOL power system. Hence, spurred by the requirement of a safe emergency landing trajectory for VTOL fixed-wing UAVs, this paper develops an architecture capable of safe emergency landing for such platforms. The suggested method develops a particle dynamics model of the VTOL UAV and analyzes its aerodynamic characteristics utilizing Computational Fluid Dynamics (CFD) results. The UAV’s trajectory is divided into three parts for enhanced planning. For the guidance stage, the initial position and heading angle are arbitrary. Hence, the Dubins shortest cross-range and the fastest descent trajectory are adopted to steer the UAV above the landing window quickly. The spiral stage comprises a conical and cylindrical part combined with a spiral descent trajectory of variable radius for energy management and landing course alignment. Given the limited energy storage of VTOL power systems, the landing stage exploits an optimal control trajectory problem solved by a Gaussian pseudospectral method, involving trajectory conventional landing planning, unpowered landing, distance optimal landing, and wind-resistant landing. All trajectories meet the dynamics constraints, terminal constraints, and sliding performance constraints and cover both 2-dimensional and 3-dimensional trajectories. A large number of simulation experiments demonstrate that the proposed trajectories manage broad applicability and strong feasibility for VTOL fixed-wing UAVs.
Full-aperture diopter distribution measurement method for single vision and progressive addition lenses

Hartmann testing method is still widely used for measuring the diopter of spectacle lens. However, its aperture measurement and resolution are limited by diffraction. This paper proposes a method to obtain full-aperture diopter distribution of spectacle lenses, including progressive addition lenses (PAL). The method uses a specially designed Hartmann testing plate for generating testing rays. The plate is rotated during measurement to overcome the diffraction limitation. Both simulation and experiment are carried out for verifying this method. The diopter measurement error of spherical power lens, cylindrical power lens, and PAL are less than 1% compared to the theoretical value in simulation. In the experiment, the deviation error is less than 2% for signal vision lens, when compared with the results obtained by an automated focimeter. The result shows that the proposed method can measure the full-aperture of diopter distribution for PAL.
Local additivity revisited

We make a number of simplifications in Gour and Friedland’s proof of local additivity of the minimum output entropy of a quantum channel. We follow them in reframing the question as one about the entanglement entropy of bipartite states associated with a dB × dE matrix. We use a different approach to reduce the general case to that of a square positive definite matrix. We use the integral representation of the log to obtain expressions for the first and second derivatives of the entropy, and then exploit the modular operator and functional calculus to streamline the proof following their underlying strategy. We also extend this result to the maximum relative entropy with respect to a fixed reference state, which has important implications for studying the superadditivity of the capacity of a quantum channel to transmit classical information.
Classification of Book Collections Based on DDC 23 Using Text Mining Algorithm at UNIDA Gontor Library

The collection of books in a library is a means of information that has become the main actor as a supporter of the existence of a library. UNIDA Gontor library uses the 23rd edition of the Dewey Decimal Classification (DDC 23) classification system, as a reference for the classification numbering system for each book collection. However, in the classification numbering there is no automatic system that helps librarians in assigning classification numbering to each collection. So it is necessary to select a suitable model system to be applied to the automatic classification system. The data used in this study is in the form of blurb data on each collection of Indonesian public books in the UNIDA Gontor Library. In this study, four methods of text mining algorithms were applied to be tested and compared. The algorithm used in testing this research is Multinomial Nb, Logistic Regression, Random Forest, and Support Vector Classifier. From the test results, the highest accuracy results are the Support Vector Classifier algorithm of 72%, while the Logistic Regression algorithm is 69%, Random Forest 69%, and Multinomial Nb 59%. Further research is recommended to apply the support vector classifier algorithm into the UNIDA Gontor library information system.
Research on Practical Training Curriculum Management for English Majors in Applied Undergraduate Universities Based on the “Production-Oriented Approach (POA)”

Production-oriented approach (POA) is a foreign language teaching theory with Chinese characteristics created by Professor Qiufang Wen. Since its establishment, it has received eager attention from foreign language experts and teachers. This research explores the management of English training curriculum based on the POA teaching concept through teaching practice, determines specific English training projects, and constructs an English training teaching process oriented by “drive-facilitate-evaluate.” A ten-week teaching experiment was conducted with 225 students from Grade Two of English major in Zhengzhou University of Industrial Technology, an applied undergraduate university in China. The teaching design, teaching plan and teaching process based on POA were demonstrated. Experiments show that the management of English training curriculum based on POA teaching concepts can help improve students’ comprehensive English language application ability and creative thinking ability.
Behavior Identification based on Improved Two-Stream Convolutional Networks and Faster RCNN

With the rapid development of deep learning in the filed of security, the application of deep learning in the factory to identify the dangerous behaviors of employees can further reduce the safety accidents caused by dangerous behaviors. Considering that the existing behavior recognition network cannot solve the problems of multi-target behavior recognition, slow detection speed and low accuracy. In this paper, a multi-target recognition model based on improved Two-Stream Convolutional Networks and Faster RCNN is proposed. The main framework of this model is based on Two-Stream Convolutional Networks. The optical flow in the temporal stream is replaced by the motion history image to reduce the computing cost. The LRCN network is introduced to replace the spatial stream network to enrich the spatial stream motion information and improve the accuracy. In this paper, the experimental research on the public data set KTH shows that the algorithm can adapt to the behavior recognition of multiple targets, with higher speed and accuracy.
Recommending Multiple Positive Citations for Manuscript via Content-Dependent Modeling and Multi-Positive Triplet

Considering the rapidly increasing number of academic papers, searching for and citing appropriate references have become a non-trial task during the wiring of papers. Recommending a handful of candidate papers to a manuscript before publication could ease the burden of the authors, and help the reviewers to check the completeness of the cited resources. Conventional approaches on citation recommendation generally consider recommending one ground-truth citation for a query context from an input manuscript, but lack of consideration on co-citation recommendations. However, a piece of context often needs to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4CR), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective to recommend multiple positive candidates. Second, we adopt noise distributions which are built based on the historical co-citation frequencies, so that MP-BERT4CR is not only effective on recommending high-frequent co-citation pairs; but also the performances on retrieving the low-frequent ones are significantly improved. Third, we propose a dynamic context sampling strategy which captures the the “macro-scoped” citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allow the algorithm to further improve the performances. Single and multiple positive recommendation experiments testified that MP-BERT4CR delivered significant improvements. In addition, MP-BERT4CR are also effective in retrieving the full list of co-citations, and historically low-frequent co-citation pairs compared with the prior works.
A Comprehensive Review on Evolving Data Stream Clustering

Most of the real-world applications deal with large data streams that are generated continuously. With this increase in the size of data streams, the existing data stream clustering algorithms cannot be implemented efficiently as these are designed to tackle static data. This invalidation of the existing algorithms becomes even more prominent when dealing with continuous as well as evolving data streams. To effectively cluster such data streams, there is a need for algorithms that can deal with the memory and computational limitations along with the concept drift. In this regard, various state-of-the-art clustering techniques have been modified to tackle the continuity and evolving nature of data. One of the main approaches used by researchers is devising a hybrid framework, commonly termed as online-offline phase clustering. This approach effectively addresses Big Data. However, it does not provide any mechanism to detect the expired data points – data that has lost its significance due to concept drift. To further improve clustering algorithms, researchers came up with window models that serve as updating units, thus, successfully handling concept drift. Realizing the need for enhanced algorithms, in this paper we closely analyze these clustering algorithms by providing a detailed discussion on their performance from different perspectives, such as memory and computational cost, cluster quality and purity, scalability, effectiveness in detecting outliers, and the cluster shape. Our survey encompasses a comprehensive and thorough literature review of the evolving data stream clustering techniques established in recent years. Further, the paper highlights the shortcomings of the algorithms, and tends to target the limitations that should be tackled in future.
A Chi-Square Based Splitting Criterion Better for the Decision Tree Algorithms

In training a Decision Tree (DTr) the choice of the criterion according to which the selection of the attribute at the level of a node is made represents a key point. The Chi-Square (CS) measure is used in one of the best known DTr algorithms: CHAID. However, this criterion tends to favour attributes with more values. In this paper I try to show that a change in this criterion can improve its performance. I will present the results of experiments performed with DTr (unpruned and pruned) on seven databases. Along with the special importance of choosing the splitting criterion, the method of pruning DTr is also perhaps as important. For this, I wanted to highlight which of the three types of DTr: unpruned DTr, pessimistic pruned DTr or error based pruned DTr, has a better behaviour for problems of classification and prediction in Data Science field. The experiments exhibited in the paper show that the modified version of the CS criterion systematically achieves better performance of the classification error rate on the test data (CERTD). At the same time, the performances acquired by DTr pruning based on confidence intervals (error-based pruned) systematically exceed the performances of the other two variants of DTr.
A Dynamic Decision Support System for Selection of Cloud Storage Provider

In recent years, the cloud computing model has gained increasing attention and popularity in the field of information technology. For this reason, people are migrating their applications to public, private, or hybrid cloud environments. Many cloud vendors offer similar features with varying costs, so an appropriate choice will be the key to guaranteeing comparatively low operational costs for an organization. The motivation for this work is the necessity to select an appropriate cloud storage provider offering for the migration of applications with less cost and high performance. However, the selection of a suitable cloud storage provider is a complex problem that entails various technical and organizational aspects. In this research, a dynamic Decision Support System (DSS) for selection of an appropriate cloud storage provider is proposed. A web-based application is implemented using PHP and MySQL to facilitate decision makers. The proposed mechanism has been optimized in a way that enables the system to address static database issues for which a user might not acquire the best solution. It focuses on comparing and ranking cloud storage providers by using two modules: scraping and parsing. The evaluation of the proposed system is carried out with appropriate test cases and compared with existing tools and frameworks.
Research on the Mixed Teaching Mode of Office Automation Course Based on OBE under the New Engineering Background

"Office Automation" is a public basic course in university curriculum, which pays attention to the combination of theory and practice. In the course implementation process, the OA course is explored in a hybrid online and offline teaching mode based on the concept of OBE. In this paper, we design a hierarchical teaching method to improve the teaching effect in a way of continuous improvement.
In Hardware We Trust? From TPM to Enclave Computing on RISC-V

System-on-Chip platforms have been increasingly extended with trusted computing functionality to provide strong protection for sensitive software applications through enclaves that only require trust in the hardware and minimal software components. However, the deployed enclave architectures are still suffering from various shortcomings such as the lack of secure I/O, or being vulnerable to side-channel attacks. Thus, recent research works propose new enclave architectures with more comprehensive threat models and advanced security features. A majority of these solutions is being developed on the open RISC-V architecture. In this paper, we present a brief overview of the RISC-V-based enclave architectures, discuss their features, limitations and open challenges.
Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V—A Practical Approach to Regression Problems

This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to download and execute in parallel to this section, as well as a simulated database of 10,000 glioblastoma patients who underwent microsurgery, and predict survival from diagnosis in months. We walk the reader through each step, including import, checking, splitting of data. In terms of pre-processing, we focus on how to practically implement imputation using a k-nearest neighbor algorithm. We also illustrate how to select features based on recursive feature elimination and how to use k-fold cross validation. We demonstrate a generalized linear model, a generalized additive model, a random forest, a ridge regressor, and a Least Absolute Shrinkage and Selection Operator (LASSO) regressor. Specifically for regression, we discuss how to evaluate root mean square error (RMSE), mean average error (MAE), and the R2 statistic, as well as how a quantile-quantile plot can be used to assess the performance of the regressor along the spectrum of the outcome variable, similarly to calibration when dealing with binary outcomes. Finally, we explain how to arrive at a measure of variable importance using a universal, nonparametric method.
Finding the global optimal solution in Dynamic multiple TSPTW with data-driven ACO

Dynamic Travelling Salesman Problem (D-TSP) is a classic dynamic optimization problem (DOP), which aims to maintain the optimal route with every change of the graph. D-TSP often greedily pursues the current optimum after each change efficiently and does not lead to the global optimum. This paper proposes a new model, Data-driven Ant Colony Optimization (D-ACO), to solve the problem by considering the historical data. We assume that some patterns can be observed from the historical data and apply these patterns to route planning. In D-ACO, artificial ants independently make up virtual vertices by sampling the data while exploring the graph. Furthermore, they remove virtual vertices after their exploration. Accumulated pheromone on the original graph carries the latent features of the actual data, which indicates the best route after the change. The experimental results on real datasets show that D-ACO can effectively identify the patterns in the historical data and outperform state-of-art models.
Progress of object detection: methods and future directions

Object detection, a significant and challenging issue in computer vision with various branches, receives great attention continuously. This paper reviews classic methods of both traditional and deep learning based detectors. Based on overall architecture and anchor, deep learning based detectors can be classified more precisely, including recent improvements. Common datasets and evaluation metrics for accuracy and speed are introduced as well. Besides, future directions, research hotspots and applications are discussed. Novel and captivated tasks such as 3D object detection, camouflaged object detection, rotated object detection are included. Anchor-free detectors are likely to be the mainstream of future object detection methods. Researches are focusing more on sophisticated situations such as small objects, occluded targets and dense distribution.