Reviewerly/exHarmony huggingface dataset:
Real-time crowd counting via mobile-friendly Vision Transformer network

Accurate crowd counting in congested scenes remain challengeable in the trade-off of efficiency and generalization. For solving this issue, we propose a mobile-friendly solution for the network deployment in high response speed demand scenarios. In order to introduce the profound potential of global crowd representations to lightweight counting model, this work suggests a novel crowd counting aimed mobile vision transformers architecture (CCMTNet), which strives for enhancing the efficiency of the model universality in real-time crowd counting tasks on resource constrained computing devices. The framework of linear CNN network interpolation structure with self-attention blocks endows the model with the ability of local feature extraction and global high-dimensional crowd information processing with low computational cost. In addition, several experimental networks with different scales based on the proposed architecture are comprehensively verified to balance the accuracy loss as compressing the computing costs. Extensive experiments on three mainstream datasets for crowd counting tasks well demonstrate the effectiveness of this proposed network. Particularly, CCMTNet achieves the feasibility of reconciling the counting accuracy and efficiency in comparisons with traditional lightweight CNN networks.
MSAGNet: crowd counting network based on multi-scale attention grading

In recent years, great progress has been made in the study of crowd counting. Although the crowd counting networks being proposed to solve different problems have achieved satisfactory counting results, the differences of crowd density and scale in the same scene still degrade the overall counting performance. In order to deal with this problem, we propose a Multi-Scale Attention Grading Crowd Counting Network (MSAGNet), which focuses on different crowd densities in the scene by attention mechanism and fuses multi-scale information to reduce scale differences. Specifically, the grading attention feature obtaining module focuses on different densities of people in the scene by attention mechanism, and adaptively assigns corresponding weights to different density regions. Dense regions are given more weights, allowing the model to focus more on that part making the training of that region more accurate and effective. In addition, the multi-scale density feature fusion module fuses the feature maps containing density information to generate the final feature maps. The obtained feature maps contain attention information at different scales, which are density mapped to obtain the estimated density maps. This method can focus on different density regions in the same scene, and simultaneously fuse multi-scale information and attention weight, which can effectively solve the problem of counting dense regions that is difficult to calculate. Extensive experiments on existing crowd counting datasets (UCF_CC_50, ShanghaiTech, UCF-QNRF) show that our method can effectively improve the counting performance.
Students’ problems in the introduction section of thesis writing

This study aims to investigate undergraduate students’ problems in writing the Introduction section of their thesis. The study used the qualitative research method, employing interview to collect the necessary data. The study took ten students of English Education Department at Universitas Muhammadiyah Aceh, Aceh Province, as the sample. The technique used to analyze the data was descriptive analysis. The results of the study showed that most problems that students faced in writing thesis in the Introduction section consisted of lack of the supporting theories, lack of paraphrasing skills, lack of motivation, lack of grammatical knowledge, and lack of writing organization skills. One of the causes of their problems was low English competence. The students, however, tried to solve their problems by having routine consultation with thesis supervisors and reading many sources related with their thesis topics.
Radiation Modeling and the Simulation of the Microorganism Disinfection in Ultraviolet Reactors

Ultraviolet disinfection can effectively destroy waterborne microorganisms. Modeling ultraviolet disinfection includes the solution of the flow field, the radiation field, and the concentration field. Radiation plays an important role in the inactivation kinetics of microorganisms. The present study presents the irradiance method to determine the irradiation flux at the lamp surface, which satisfies the energy conservation law. A careful analysis shows that the fluence rate method may overestimate the emitted energy of the lamp nearly 30–40% and violates the energy conservation law. The simulation of ultraviolet reactors shows that the fluence rate is overestimated by the fluence rate method. The concentration of microorganisms in ultraviolet reactors is also overestimated nearly 32% by the fluence rate method than by the irradiance method. The LOG reduction predicted using the fluence rate method is nearly 50% more than that using the irradiance method. The present irradiance method can ensure energy conservation and predict the correct disinfection performance in ultraviolet reactors.
Development of Canva Aplication based E–Magazine on Static Fluids to Improve Student Motivation

This study aims to test the feasibility of E-magazine learning media on static fluid materials to increase student motivation. This research method is ADDIE development model. The assessment instrument used in this study was an expert validation sheet which consisted of content aspects, presentation aspects, linguistic aspects, media aspects and motivational aspects and student perception sheets which consisted of several aspects, namely, material aspects, language aspects, and media aspects. Based on the results of expert validation from. Based on the results of the product validation test, the validator gets an overall average percentage of 86.92% with a very decent category. Based on the results of students' perceptions of the E-magazine, it can be said to be very good in the three Schools, with an overall average percentage of 87.22%. So it can be concluded that learning media products based on E-magazines on static fluid material to increase student learning motivation are very feasible to use.
Concluding Remarks

The final chapter of the 1st edition of this book (hereafter ECL1), “Future Prospects in Corpus Linguistics,” began with the following paragraph
Optimal Software Based Sign Language Recognition System

According to the report, more than 5% of the population is deaf. The ability to communicate with people is impacted by deafness. Deaf people use sign language to communicate with one another. Hand movement is the basis of sign language. Normal people, on the other hand, rarely learned sign language, making communication difficult in the actual world. This paper discusses the development of research in the subject of sign language identification, which aids deaf individuals in society in communicating. It has been analyzed and discussed the future potential of many problems that aid in the detection of sign language.
Study on the Acquisition Strategies of Chinese as a Second Language in Different Environments

With the vigorous development of Chinese teaching today, the research on Chinese as a second language acquisition strategy is increasingly abundant in China. But due to the COVID-19 pandemic, many Chinese language teaching activities have been forced to move online. Most Chinese learners live abroad in the environment of non-target language. What are the similarities and differences in the acquisition strategies of Chinese learners in different environments? Based on previous studies, this paper will conduct a comparative analysis of the acquisition strategies adopted by Chinese learners in different environments to explore their commonalities. In order to help international Chinese teachers clear teaching strategies, promote the better development of international Chinese teaching cause.
Research of the Influence of the Fuzzy Rules Number on the Learning of a Neuro-Fuzzy System

The accuracy of fuzzy logic systems is measured by the RMSE (Root Mean Square Error), with lower values indicating greater precision. ANFIS (Adaptive Neuro-Fuzzy Interference Systems) is employed to minimize the RMSE. ANFIS algorithms are based on the traditional fuzzy logic models of Mamdani and Sugeno and use hard arithmetic operations during finding minimum and maximum values. The RMSE can be reduced several times by adding of fuzzy rules to the fuzzy inference. Furthermore, the use of soft arithmetic operations in the fuzzy system can reduce the RMSE without used the ANFIS models. This chapter outlines the analytic expressions that demonstrate the proposed method's effectiveness.
The motivation of Adolescents to Choose a Secondary Vocational School

The authors reflect on the motivation of adolescents to attend secondary vocational schools. For adolescents, this step represents one of the fundamental career decisions. The intrinsic motivation of applicants to study is often interest in the field, employment, or financial reward. Other motivating factors for studying are the transport accessibility of the school and the difficulty of the admission procedure. Aspiring secondary school students are also motivated by the prestige of the school that offers them an educational programme. In addition to the well-known promotions, adolescents can also attend extracurricular activities (hobbies) organised in secondary vocational schools. The authors make their statements based on the opinions of primary school teachers and secondary school students who evaluate their motivation for education in their field of study. At the end of the paper, the authors evaluate and discuss the originality of the possible promotion of secondary vocational education.
A Novel Approach for Enhance Fusion Based Healthcare System In Cloud Computing

Individuals start - ups and large corporations in the healthcare sector have new opportunities to outsource data and outsourcing computation offers to cloud computing. Although the cloud computing paradigm presents users with interesting and cost effective opportunities still in its early stage, and using the cloud introduces with new obstacles. A another issue is the security of cloud data, which may be affected the data particularly in the case of healthcare systems that store and process sensitive data and is outsourced to a cloud computing system.Although there has been significant progress in the development of health services there are still issues that need to be settled regarding, integrity, the security, large-scale deployment, service integration, confidentiality of sensitive medical data. To ensure that sensitive medical data is captured, stored and consumed securely, an information sharing policy syntax based on rules, the Data Capture and Auto Identification Reference (DACAR) platform features a Single Point of Contact as well as data buckets that are hosted on a cost-effective cloud infrastructure and scalable.As a result, security, accuracy, and precision are achieved in this analysis and query time is reduced.
PatchNet: Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation

With the increase in the number of image data and the lack of corresponding labels, weakly supervised learning has drawn a lot of attention recently in computer vision tasks, especially in the fine-grained semantic segmentation problem. To alleviate human efforts from expensive pixel-by-pixel annotations, our method focuses on weakly supervised semantic segmentation (WSSS) with image-level labels, which are much easier to obtain. As a considerable gap exists between pixel-level segmentation and image-level labels, how to reflect the image-level semantic information on each pixel is an important question. To explore the congeneric semantic regions from the same class to the maximum, we construct the patch-level semantic augmentation network (PatchNet) based on the self-detected patches from different images that contain the same class labels. Patches can frame the objects as much as possible and include as little background as possible. The patch-level semantic augmentation network that is established with patches as the nodes can maximize the mutual learning of similar objects. We regard the embedding vectors of patches as nodes and use a transformer-based complementary learning module to construct weighted edges according to the embedding similarity between different nodes. Moreover, to better supplement semantic information, we propose soft-complementary loss functions matched with the whole network structure. We conduct experiments on the popular PASCAL VOC 2012 and MS COCO 2014 benchmarks, and our model yields the state-of-the-art performance.
Technologies of the Fourth Industrial Revolution and their reflection in general education courses of computer science and technology

The article discusses the main technologies of the 4th industrial revolution: the Internet of things, robotics, 3D printing, big data, etc. These technologies play an important and growing role in modern professional activity. In the context of technologies of the 4-industrial revolution, a key role is given to information and cognitive technologies as a necessary condition for the development and use of "smart technologies". The general educational aspects of these technologies must necessarily be included in the content of general educational subjects. The main subjects in this case are the subjects of computer science and technology.
Multi-authority (t,n) threshold identity-based matchmaking encryption

In this work, we propose a Multi-authority (, ) t n Threshold Identity-based Matchmaking Encryption (MAT-IBME) scheme under the standard assumptions in the standard model, in which multiple authorities share the secrets for constructing users’ secret keys. The MAT-IBME scheme satisfies the requirement that the receiver can only get the decryption key with the "joint permission" of t ones among n authorities. Meanwhile, only when the identity of the sender satisfies the policy proposed by the receiver, and the identity of the receiver satisfies the policy proposed by the sender, the receiver can decrypt correctly. In this way, the scheme realizes precise ciphertext access control when deployed in cloud services. Security and performance analysis shows that MAT-IBME not only achieves authenticity security but also system-level robustness when no less than t authorities are alive in the system.
States, Modes, Fields, and Photons in Quantum Optics

The quantum nature of light enables potentially revolutionary communication technologies.A key to advancing this area of research is a clear understanding of the concepts of states, modes, fields, and photons.The concept of field modes carries over from classical optics, while the concept of state has to be considered carefully when treating light quantum mechanically.The term "photon" is an overloaded identifier in the sense that it is often used to refer to either a quantum particle or the state of a field.This overloading, often used without placing it in context, has the potential to obfuscate the physical processes that describe the reality we measure.We review the uses and relationships between these concepts using modern quantum optics theory, including the concept of a photon wave function, the modern history of which was moved forward in a groundbreaking paper in this journal by Iwo Białynicki-Birula, to whom this article is dedicated.
Signature method for determination of the lunar lander position by video image

The paper considers the problem of developing a visual navigation system to determine the proper position of the lunar lander. The paper proposes a new method for comparing the observed image frame with a vector map of the Moon based on the comparison of signatures. Experiments show that the proposed method is able to work in real time, is resistant to lighting conditions, small changes in the camera angle, scale and noise, and is able to work with a large number of gaps and false positives of the crater detector. The proposed method can be used in modern domestic and international space programs for the exploration of the Moon to ensure a soft high-precision safe landing of the lunar lander in a given area of the Moon.
A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation

&lt;abstract&gt; &lt;p&gt;The utilization of intelligent computing in digital teaching quality evaluation has been a practical demand in smart cities. Currently, related research works can be categorized into two types: textual data-based approaches and visual data-based approaches. Due to the gap between their different formats and modalities, it remains very challenging to integrate them together when conducting digital teaching quality evaluation. In fact, the two types of information can both reflect distinguished knowledge from their own perspectives. To bridge this gap, this paper proposes a textual and visual features-jointly driven hybrid intelligent system for digital teaching quality evaluation. Visual features are extracted with the use of a multiscale convolution neural network by introducing receptive fields with different sizes. Textual features serve as the auxiliary contents for major visual features, and are extracted using a recurrent neural network. At last, we implement the proposed method through some simulation experiments to evaluate its practical running performance, and a real-world dataset collected from teaching activities is employed for this purpose. We obtain some groups of experimental results, which reveal that the hybrid intelligent system developed by this paper can bring more than 10% improvement of efficiency towards digital teaching quality evaluation.&lt;/p&gt; &lt;/abstract&gt;
Think on Teaching Evaluation on Chinese University Teachers Based on Cloud Platform Data Evidence

At present, the teaching evaluation of most universities in China is mainly based on the students’ evaluation of teachers at the end of the learning course, which is difficult to play a role in the continuous improvement of teaching. Based on the Outcome-Based Education (OBE) teaching concept, this paper considered and designed a teacher teaching evaluation system based on the data evidence of cloud teaching platform. The system can save all the teaching activities and documents of teachers before, during and after class as well as the learning results of students. The evaluator used the cloud platform teaching documents to evaluate the teaching quality of subject teachers. The subject teacher will receive the evaluation results and follow-up teaching reform suggestions in the teaching evaluation module, and adjust and optimize the follow-up lectures in time to continuously improve the teaching quality. Finally, this paper gave the strategies for Chinese universities to implement teacher teaching evaluation based on cloud teaching platform data evidence, in order to better promote the reform of teacher teaching evaluation in Chinese universities.
City scale autonomy learning

Reinforcement learning for agent autonomous actions requires many repetitive trials to succeed. The idea of this paper is to distribute the trials across a city-scale geospatial map. This has the advantage of providing rationale for the trial-totrial variance because each location is slightly different. The technique can simultaneously train the agent and deduce where difficult and potentially dangerous intersections exist in the city. The concept is illustrated using readily available open-source tools.
Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity

The ever-growing size, breadth, and availability of patient data allows for a wide variety of clinical features to serve as inputs for phenotype discovery using cluster analysis. Data of mixed types in particular are not straightforward to combine into a single feature vector, and techniques used to address this can be biased towards certain data types in ways that are not immediately obvious or intended. In this context, the process of constructing clinically meaningful patient representations from complex datasets has not been systematically evaluated.Our aim was to a) outline and b) implement an analytical framework to evaluate distinct methods of constructing patient representations from routine electronic health record data for the purpose of measuring patient similarity. We applied the analysis on a patient cohort diagnosed with chronic obstructive pulmonary disease.Using data from the CALIBER data resource, we extracted clinically relevant features for a cohort of patients diagnosed with chronic obstructive pulmonary disease. We used four different data processing pipelines to construct lower dimensional patient representations from which we calculated patient similarity scores. We described the resulting representations, ranked the influence of each individual feature on patient similarity and evaluated the effect of different pipelines on clustering outcomes. Experts evaluated the resulting representations by rating the clinical relevance of similar patient suggestions with regard to a reference patient.Each of the four pipelines resulted in similarity scores primarily driven by a unique set of features. It was demonstrated that data transformations according to each pipeline prior to clustering can result in a variation of clustering results of over 40%. The most appropriate pipeline was selected on the basis of feature ranking and clinical expertise. There was moderate agreement between clinicians as measured by Cohen's kappa coefficient.Data transformation has downstream and unforeseen consequences in cluster analysis. Rather than viewing this process as a black box, we have shown ways to quantitatively and qualitatively evaluate and select the appropriate preprocessing pipeline.