Reviewerly/exHarmony huggingface dataset:
A New Method of Constructing Readability Standard Corpus for International Chinese Teaching

In order to solve the problem of low degree of computer automation in the establishment of readability standard corpus for international Chinese teaching. Combined with the practice of international Chinese teaching, this paper discussed two methods to divide the grades of text: according to the proportion of Hanyu Shuiping Kaoshi (HSK) words in the text; according to the circulation degree in the texts. The Chinese language proficiency test (HSK) is an international Chinese proficiency standardized test. Compared with the two methods, the second method is finally feasible. Therefore, this paper rearranges the grades of 389 teaching texts according to this method, and divides the text into a new readability grade according to the quantitative characteristics of each set of teaching materials. This method is a more scientific way to construct the readability standard corpus for international Chinese teaching. The scientific establishment of readability corpus is of great significance to study the formal characteristics and attributes of Chinese text information and knowledge, as well as the related processes of communication between people and machines. It also contributes to the research of document metrology and information metrology, and is also conducive to the research, search and use of human demand for information.
A Meta-analysis of The Effects of University-Industry Cooperative Education on Talent Cultivation Performance

This paper investigates the impact of university-industry cooperative education on talent cultivation, using data from a sample of 22,018 of 34 empirical research papers over the period 2011-2021. We innovatively use meta-analysis, combined with the USEM model. It is demonstrated that cooperative education has an important positive effect on talent cultivation performance, both in terms of the breadth and depth of cooperative education. The results confirm that the talent cultivation performance measurement indicators, school category, and region have a significant moderating effect on the relationship between cooperative education and talent cultivation performance, while the intervening variable does not play a significant moderating role.
Chinese Children's Chinese-English Bilingual Acquisition Mode: A Comparative Experiment of AI Educational Robot and Scan Reading Pen

Due to the lack of an English learning environment, Chinese children generally have a weak command of English. Therefore, 164 fifth-grade primary school children in Hangzhou, China, were chosen to participate in a five-month bilingual acquisition comparative experiment. During the experiment, 83 students employed AI teaching robots, whereas 81 students used scan reading pens. The acquisition method chosen was role-play reading, and the acquisition materials used were multimedia bilingual electronic picture books. The experimental results reveal that the average daily bilingual reading time of students in the two groups increased five to ten times respectively, and their final English and Chinese test scores increased by 7-8% and 3-4% on average, their oral English test scores increased by 81% and 48%. Additionally, their bilingual pronunciation and intonation improved by 2-3 grades, indicating that the AI educational robot mode can be used to improve bilingual learning outcomes, particularly their level of spoken language. This experiment shows that the AI-assisted bilingual acquisition is a practical, affordable, and widely applicable bilingual educational mode for children.
A Recommender System for Communication Apps by Analyzing Reviews

Due to the impact of Covid-19, various means of virtual communication such as online education and online teamwork have become increasingly popular under different circumstances. However, it is difficult to select the one that suits your needs fast from a large number of online communication apps. Therefore, this study is aimed to build a system that is capable to recommend online communication apps and meet the needs of users through analysis of app reviews. Despite attempts made in some of the existing studies to recommend apps using the negative/positive information carried by reviews and the historical data of users, it cannot be said that the use of textual information included in reviews is directly linked to user needs. This study built a system that is capable to recommend online communication apps and meet the needs of users through analysis of app reviews. In this research, by employing the user requests (hereinafter referred to as "request sentences") and app review data extracted in advance, the online communication apps that match the user needs are output in a ranking list.
Topic Detection and Tracking Towards Determining Public Agenda Items

It is a known fact that all of the events that people in the society are exposed to while continuing their lives have important effects on their quality of life. Events that have significant effects on a large part of the society are shared with the public through news texts. With a perspective that keeps up with the digital age, the problem of automatic detection and tracking of events in the news with natural language processing methods is discussed. An event-based news clustering approach is presented for data regimentation, which is necessary to extract meaningful information from news in the form of heaps in online environments. In this approach, it is aimed to increase clustering performance and speed by making use of named entities. Additionally, an event-based text clustering dataset was created by the researchers and brought to the literature. By using the B-cubed evaluation metric on this test dataset, which consists of 930 different event groups and has a total of 19,848 news, a solution to the event-based text clustering problem was provided with an F-score of over 85%.
Semantic Tagging of Events in Video Using HNN

This chapter describes the semantic tagging of events in videos using an effective combination of machine learning and neural network. Hybrid neural network architecture is proposed to consider the object features generated for each video and combine them with the LSTM model running over the label. The entire system is highly efficient for training and learning as the training dataset is optimized by applying multiple machine learning techniques. Experiments were done on the KTH dataset. Results show that the approach used gives 97% accuracy for the KTH dataset.
Deep Learning for Information Extraction From Digital Documents

Print-oriented PDF documents are excellent at preserving the position of text and other objects but have difficulties in processing. Processable PDF documents will provide solutions to the unique needs of different sectors by paving the way for many innovations such as searching within documents, linking with different documents, or restructuring in a format that will increase the reading experience. In this chapter, a deep learning-based system design is presented that aims to export clean text content, separate all visual elements, and extract rich information from the content without losing the integrated structure of content types. While the F-RCNN model using the Detectron2 library was used to extract the layout, the cosine similarities between the wod2vec representations of the texts were used to identify the related clips, and the transformer language models were used to classify the clip type. The performance values on the 200-sample data set created by the researchers were determined as 1.87 WER and 2.11 CER in the headings and 0.22 WER and 0.21 CER in the paragraphs.
Machine Learning in Healthcare

Machine learning approaches are utilized in healthcare for computational decision-making in cases where critical medical data analysis is required to identify hidden linkages or anomalies that are not evident to humans. Artificial intelligence (AI) tools can assess a wide range of health data; patient data from multi-omics methods; clinical, behavioural, environmental, pharmacological data; and data from the biomedical literature to respond to research issues that necessitate a big sample size on a difficult-to-reach population. In healthcare, digitising health data has eased the development of computational models and AI systems to extract insights from the data. This chapter initially addressed the prospectus of machine learning in public health with significant focus areas. The medical devices and equipment section contain device-based modelling approaches to various diseases. The chapter also includes brief details on chatbots, wearable technologies, drug distribution systems, vending machines, and text recognition from prescriptions and medicine boxes are addressed.
Digital Forensic Techniques and Principles in a Cloud Environment

Digital forensic methods for cybercrime investigation in a cloud computing environment (i.e., cloud forensic) is a broad term that encompasses a number of different fields, including digital forensics and cloud computing. The authors hope to gain a better understanding of cloud computing and identify challenges and opportunities in forensic science. They present research that may be used to establish strategies for the creation of measures, standards, and technological research to minimise the issues that cannot be solved with current technology and methodologies. Among the topics covered in this chapter are cloud computing, digital forensics, cybercrimes, and cloud forensics. Also covered are an overview and principles of cloud computing as well as digital forensics, cybercrimes, and challenges, as well as opportunities in the field of cloud forensics. The state of the art in the cloud forensic domain is also presented, as is a literature review on related work to digital forensics techniques in the cloud computing environment.
Margin embedding net for robust margin collaborative representation-based classification

Collaborative Representation-based Classification method (CRC) shows great potential in classification task. However, redundancies in both features and samples limit the application of CRC seriously. The existing works only solve one of them and ignore the other, which leads to performance degradation. To address this problem, we explore collaborative representation mechanism and propose a classification method termed Robust Margin Collaborative Representation-based Classification (RMCRC) which uses a few but more representative robust marginal samples to eliminate redundancy between samples. As the performance of RMCRC is related to robust marginal samples and class separability assumption closely, we further propose a feature extraction method termed Margin Embedding Net (MEN) for RMCRC. In MEN, virtual samples are generated by a generative model to enhance effectiveness of robust marginal samples and generalizability of RMCRC. Then, an embedding network with triplet loss is used to eliminate the redundancy in features and ensure the assumption is satisfied. Specifically, we construct triplet according to the collaborative representation. Hence, MEN fits RMCRC very well. Extensive experimental results validate effectiveness of proposed method.
SIM-MFR: Spatial interactions mechanisms based multi-feature representation for background modeling

Moving object detection is frequently used as a springboard for advanced computer vision analysis in complex scenes. Nevertheless, due to unstable changes in the background, most existing background model hardly maintain superior performance. To this concern, we propose a novel pixel-level background model that has three innovations. First, we introduce K-means to directly model the spatiotemporal dependencies between pixels. These dependencies are exploited to discover static core information in the high-frequency changing spatial domain, resulting in excellent property in dynamic backgrounds. Besides, the notion of complementarity is taken as a feature selection criterion. In multi-feature model, the ability to supervise each other between features is important in the ambiguity challenges, e.g., shadow. Finally, feature models recommend each other in the update mechanism, and the diffusion rate of effective information in each feature model can be maximized by finding the best candidate feature. By virtue of this mechanism, model can be updated efficiently when large background migration occurs, e.g., PTZ. Experimental results on some standard benchmarks show that SIM-MFR can achieve promising performance compared to some state-of-the-art approaches.
Usage Quality Assessment of Weft Knitted Fabrics Made of Modal Fibers

Knitwear that are worn in direct contact with the skin, are often made of man-made artificial fibers from cellulose which provide silky touch and exceptional contact comfort. In this paper three different circular weft double jersey knitted fabrics made of single modal ring, rotor and air-jet spun yarns of the same linear density, all made of bright staple modal fibers of linear density of 1.3 dtex and length of 38/40 mm, were used. With the purpose of analyzing the influence of differently spun modal yarns and the knitted fabrics processing level on their properties, along with the basic characterization of yarns, the usage quality of raw and finished knitted fabrics were evaluated and their applicability assessed.
Online beam orbit correction of MEBT in CiADS based on multi-agent reinforcement learning algorithm

The proton linac accelerator has been the mostly appropriate equipment applied to radioactive waste disposal and cancer treatment. However, beam offset is a particularly significant problem during proton acceleration, which not only produces a halo and causes orbital distortion of the beam to make the beam injection inefficient. In exceptionally complex accelerator systems, accurate correction of beam offset is very difficult. The traditional method is based on manual experience and manual adjustment to achieve beam trajectory correction, the automation level is low and the adjustment speed is slow. Therefore, this paper proposes a new method of beam orbit correction based on Multi-Agent Reinforcement Learning. In order to verify the feasibility of the algorithm in beam orbit correction, this method takes the MEBT section of the virtual accelerator as the control object, and uses three agents to control the beam orbit of Medium Energy Beam Transport (MEBT) in sections. Each agent is designed based on the Reinforcement Learning of the Deep Deterministic Policy Gradient (DDPG) algorithm, the deterministic strategy is used to explore the large state and action space to realize discretization, and the training model convergence is successfully realized. The experimental results show that the final calibrated average beam offset of this method is about 0.39 mm, which achieves the expected experimental goal. It can control the beam orbit of MEBT, and has application value and competitiveness in the beam orbit correction of linac.
Research on Mono-Pulse Beam Angle Tracking Algorithm of Phased Array Antenna Based on CKF

Phased array antenna beam tracking and communication system based on mono-pulse system is an alternative satellite-to-earth communication technology solution for future planetary spacecraft, which meets the mission requirements of agile scanning, multimode, wide field of view, and small size. This paper firstly outlines the technical scheme of mono-pulse angle tracking based on the planar phased array antenna. A simplified mathematical model of angle measurement is summarized. Secondly, the beam pointing of the spacecraft communication antenna is frequently affected by the attitude and presents a nonlinear dynamic change. A robust two-dimensional angular tracking filtering algorithm based on cubature Kalman filter (CKF) is proposed, in order to solve this complex high-order nonlinear dynamic estimation problem. Finally, the comprehensive performance of the proposed algorithm is simulated and analyzed under two conditions of stable flight and high dynamic flight of the spacecraft. When the angular velocity is within 4°/s, the tracking accuracy can be better than 0.65°, and the tracking accuracy will be better than 1° when the angular velocity is within 8°/s. Compared with the basic CKF algorithm, the robust CKF tracking accuracy is significantly improved, while the resource consumption is slightly reduced. The research results can provide reference for the engineering implementation of related fields in the future.
Projecting spatiotemporal variations of sunshine duration with regards to climate change in Iran as a step towards clean energy

Iran is a country with great potential for using solar energy. To manage the supply and demand for using solar energy technologies, proper knowledge regarding the areas with homogeneous sunshine duration is essential. Therefore, in this study, the monthly data anomalies were used to determine homogeneous climatic regions over two study periods. The matrix grids were prepared for the base (1981–2018) and future (2041–2080) periods respectively as 456×272 and 480×272 (the number of months and stations). Notably, the future period model was prepared according to two separate Representative Concentration Pathway (RCP) scenarios (4.5 and 8.5). The S-mode input matrix was used to perform principal component analysis (PCA) and Ward’s method was utilized for cluster analysis (CA). Subsequently, the stations were partitioned into different sunshine clusters. Findings showed that 86.6 % of the total variance were involved with seven leading PCs (7 clusters for the base period), 67.2 % with four initial RCP4.5 components, and 62.8 % with the first three RCP8.5 components (4 and 3 clusters for the future period, respectively). Concerning the sunshine duration, results indicated geographical latitude as the most important factor for the spatial distribution of different clusters. Additionally, global warming was seen to have caused an increase in sunshine hours for 73 and 182.5 hr/year according to RCP4.5 and RCP8.5 compared to the base period. Therefore, an overview of how climate change could affect sunshine duration in Iran enhances the potential for efficient use of clean solar energy.
Automating model management: a survey on metaheuristics for concept-drift adaptation

Abstract This study provides an overview of the literature on automated adaptation of machine learning models via metaheuristics, in settings with concept drift. Drift-adaptation of machine learning models presents a high-dimensional optimisation problem; hence, stochastic optimisation via metaheuristics has been a popular choice for finding semi-optimal solutions with low computational costs. Traditionally, automated concept drift adaptation has mainly been studied in the literature on data stream mining; however, as data drift is prevalent in many areas, analogous solutions have been proposed in other fields. Comparing the conceptual solutions across multiple fields is thereby helpful for the overall progress in this area. The found literature is qualitatively classified in terms of relevant aspects of concept drift, adaptation/automation approach and type of metaheuristic. It is found that population-based metaheuristics are by far the most widely used optimisation methods across the domains in the retrieved literature. Methodological problems such as evaluation method and transparency in terms of concept drift type tested in the experiments are discovered and discussed. Over a ten-year period, the usage of metaheuristics in the found literature transitioned from automating single tasks in model development to full model selection in recent years. More transparency in terms of evaluation method and data characteristics is important for future comparison of solutions across drift types and patterns. Furthermore, it is proposed that future studies in this area evaluate the metaheuristics as models themselves, in order to enhance the general understanding of their performance differences in drift adaptation problems.
PS+: A Simple yet Effective Framework for Fast Training on Parameter Server

In distributed training, workers collaboratively refine the global model parameters by pushing their updates to the Parameter Server and pulling fresher parameters for the next iteration. This introduces high communication costs for training at scale, and incurs unproductive waiting time for workers. To minimize the waiting time, existing approaches <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overlap communication and computation</i> for deep neural networks. Yet, these techniques not only require the layer-by-layer model structures, but also need significant efforts in runtime profiling and hyperparameter tuning. To make the overlapping optimization <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simple</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generic</i> , in this article, we propose a new Parameter Server framework. Our solution <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decouples</i> the dependency between push and pull operations, and allows workers to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eagerly</i> pull the global parameters. This way, both push and pull operations can be easily overlapped with computations. Besides, the overlapping manner offers a different way to address the straggler problem, where the stale updates greatly retard the training process. In the new framework, with adequate information available to workers, they can explicitly modulate the learning rates for their updates. Thus, the global parameters can be less compromised by stale updates. We implement a prototype system in PyTorch and demonstrate its effectiveness on both CPU/GPU clusters. Experimental results show that our prototype saves up to 54% less time for each iteration and up to 37% fewer iterations for model convergence, achieving up to 2.86× speedup over widely-used synchronization schemes.
The effectiveness of risk assessments in risk workshops: the role of calculative cultures

This paper investigates drivers of the effectiveness of risk assessments in risk workshops dominated by ‘quantitative skepticism’. Moreover, it contrasts our findings with those of previous research that assumed the dominance of ‘quantitative enthusiasm’. Quantitative skepticism is a calculative culture characterized by an attitude that regards risk assessments as learning tools supporting the holistic formation of judgments incorporating difficult-to-quantify information. It contrasts with quantitative enthusiasm, which is a calculative culture that considers risk assessments as fully descriptive of reality. Prior research primarily focused on understanding the effectiveness of risk assessments under a calculative culture of quantitative enthusiasm. To understand what drives the correctness of risk assessment and the time needed to assess risks in workshops under a calculative culture of quantitative skepticism, we use an agent-based model that simulates risk assessment with risk workshops and that models agents’ cognitive processes using ECHO, a constraint satisfaction network (CSN). Our simulations show that, compared to risk workshops under conditions of quantitative enthusiasm, there are often lengthy periods of stagnation in individual and collective risk assessments and a strong path dependency on discussions. Prioritizing concerned participants improves the correct assessment of high risks at the expense of the correct assessment of low risks. Notwithstanding similarities in the drivers of the effectiveness of risk assessment across different calculative cultures, our results show that the predominant calculative culture matters when—to enhance their effectiveness—designing and implementing risk workshops.