Reviewerly/exHarmony huggingface dataset:
Optimization of the Storage Spaces and the Storing Route of the Pharmaceutical Logistics Robot

Auto drug distribution systems are used popularly to replace pharmacists when drugs are distributed in pharmacies. The Cartesian robot is usually used as the recovery mechanism. Under non-dynamic storage location conditions, generally, the selected planning route of the Cartesian robot is definite, which makes it difficult to optimize. In this paper, storage spaces were distributed for different drugs, and the route of storing was broken down into multiple path optimization problems for limited pick points. The path was chosen by an improved ant colony algorithm. Experiments showed that the algorithm can plan an effective storing route in the simulation and actual operation of the robot. The time spent on the route by improved ant colony algorithm sequence (IACS) was less than the time spent of route by random sequence (RS) and the time spent of route by traditional ant colony algorithm sequence (ACS); compared with RS, the optimized rate of restoring time with iacs can improve by 22.04% in simulation and 7.35% in operation. Compared with ACS, the optimized rate of restoring time with iacs was even more than 4.70% in simulation and 1.57% in operation. To the Cartesian robot, the optimization has certain guiding significance of the application on the 3D for improving quality.
Quantum-coherence-free precision metrology by means of difference-signal amplification

The novel weak-value-amplification (WVA) scheme of precision metrology is deeply rooted in the quantum nature of destructive interference between the pre- and post-selection states. And, an alternative version, termed as joint WVA (JWVA), which employs the difference-signal from the post-selection accepted and rejected results, has been found possible to achieve even better sensitivity (two orders of magnitude higher) under some technical limitations (e.g. misalignment errors). In this work, after erasing the quantum coherence, we analyze the difference-signal amplification (DSA) technique, which serves as a classical counterpart of the JWVA, and show that similar amplification effect can be achieved. We obtain a simple expression for the amplified signal, carry out characterization of precision, and point out the optimal working regime. We also discuss how to implement the post-selection of a classical mixed state. The proposed classical DSA technique holds similar technical advantages of the JWVA and may find interesting applications in practice.
Report from the VII Polish National Student Conference "YOUNG FOR SAFETY" Gorzw Wielkopolski, April 22, 2022

The conference aims to meet young scientists, i.e. PhD students and students, to present their research interests. During the conference, the speakers raise issues related to broadly understood security. This year's conference was the seventh in a series of annual scientific meetings devoted to this subject
High-Precision 3D-DIC Measurement Method Based on Improved Forward Newton Iteration

To solve the problems of the traditional 3D-DIC algorithm based on feature information or FFT search at the expense of accuracy in exchange for time, such as error-point extraction, mismatching of feature points, poor robustness, and accuracy loss caused by poor anti-noise performance, an improved high-precision 3D-DIC measurement method was proposed. In this method, the exact initial value is obtained by an exhaustive search. Then, the forward Newton iteration method is used for pixel classification, and the first-order nine-point interpolation is designed, which can quickly obtain the elements of Jacobian and Hazen matrix, and achieve accurate sub-pixel positioning. The experimental results show that the improved method has high accuracy, and its mean error and standard deviation stability and extreme value are better than similar algorithms. Compared with the traditional forward Newton method, the total iteration time of the improved forward Newton method is reduced in the subpixel iteration stage, and the computational efficiency is 3.8 times that of the traditional NR algorithm. The whole process of the proposed algorithm is simple and efficient, and it has application value in the precision occasions requiring high precision.
An attention-embedded GAN for SVBRDF recovery from a single image

Abstract Learning-based approaches have made substantial progress in capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) from a single image with unknown lighting and geometry. However, most existing networks only consider per-pixel losses which limit their capability to recover local features such as smooth glossy regions. A few generative adversarial networks use multiple discriminators for different parameter maps, increasing network complexity. We present a novel end-to-end generative adversarial network (GAN) to recover appearance from a single picture of a nearly-flat surface lit by flash. We use a single unified adversarial framework for each parameter map. An attention module guides the network to focus on details of the maps. Furthermore, the SVBRDF map loss is combined to prevent paying excess attention to specular highlights. We demonstrate and evaluate our method on both public datasets and real data. Quantitative analysis and visual comparisons indicate that our method achieves better results than the state-of-the-art in most cases.
Entrepreneurial Education Model Based on Interactive Technology and Cognitive Psychology

In order to encourage college students to innovate and start businesses in order to alleviate the increasingly serious employment pressure, all sectors of society attach great importance to the cultivation of College Students’ entrepreneurial ability. However, due to the lack of social presence awareness of learners, the interaction willingness of entrepreneurship education model is low, and the teaching effect is not strong. This article takes “learner interaction” as the core, takes the design of collaborative problem-solving learning activities as the starting point, and selects four influencing factors of the importance of learners in the interactive network, including point centrality, intermediary centrality, proximity centrality, and the object of interaction, from the macro, meso, and micro levels of cohesion of the interactive network. Based on the level of social presence, the core content of the interaction model of information perception, emotional experience and feedback exchange is determined according to social presence, and an interaction model to improve students’ social presence in online learning is constructed. The example verification shows that this method can significantly improve the learning participation of entrepreneurship education learners, and the on-the-spot manifestation of learners’ social practice is more significant.
Semantic-visual shared knowledge graph for zero-shot learning

Almost all existing zero-shot learning methods work only on benchmark datasets (e.g., CUB, SUN, AwA, FLO and aPY) which have already provided pre-defined attributes for all the classes. These methods thus are hard to apply on real-world datasets (like ImageNet) since there are no such pre-defined attributes in the data environment. The latest works have explored to use semantic-rich knowledge graphs (such as WordNet) to substitute pre-defined attributes. However, these methods encounter a serious "role="presentation">domain shift" problem because such a knowledge graph cannot provide detailed enough semantics to describe fine-grained information. To this end, we propose a semantic-visual shared knowledge graph (SVKG) to enhance the detailed information for zero-shot learning. SVKG represents high-level information by using semantic embedding but describes fine-grained information by using visual features. These visual features can be directly extracted from real-world images to substitute pre-defined attributes. A multi-modals graph convolution network is also proposed to transfer SVKG into graph representations that can be used for downstream zero-shot learning tasks. Experimental results on the real-world datasets without pre-defined attributes demonstrate the effectiveness of our method and show the benefits of the proposed. Our method obtains a +2.8%, +0.5%, and +0.2% increase compared with the state-of-the-art in 2-hops, 3-hops, and all divisions relatively.
Librarian’s workplace in the context of the library’s facilities and equipment: Theoretical approach and practical realization

The facilities and resources of the libraries determine their performance, therefore the development and improvements of physical facilities and librarians’ workplace must always be in the focus, along with the library’s functions, structure, and vectors of activities. The efficiently designed, comfortable, organized and technological workplace increases productivity. The latest achievements of science, technology and ergonomics contribute to efficient infrastructure, premises and collection utilization, and employees’ performance. Innovative workplace arrangement stimulates employees to develop interactive and other communications and to meet modern users’ demands to the fullest extent possible. The authors prove the necessity of developing library facilities and resources taking into account the state of modern society, science and technology. For this purpose, the librarians, architects, engineers, furniture manufacturers, designers and local authorities must join their efforts. The authors present the findings of the research based on the librarians’ survey and identify their preferences and ideal workplace image.
Digital services at the affiliate libraries of the Russian Academy of Sciences to provide information support of science (the case study of Pushchino Central Library)

The national strategy for Russian libraries up to the year 2030 was approved by the RF Government in 2021. The development vectors extend to every library in the country, including scientific and academic ones. The author reviews the library system of the Russian Academy of Sciences and evaluates the possibility to implement digital technologies. For this purpose, she analyzes the existing computerized information library systems in centralized library networks ( CLN). T he R AS l ibrary s ystem h as t he p otential t o i mprove i nformation support of research. The structure of the RAS library network replicates the regional and discipline-oriented organizational RAS structure. Based on the experience of Pushchino Central Library – department of the RAS Library for Natural Sciences, the author concludes that despite incongruous software, hardware and technology, the scientific libraries have the potential to improve library services to support research. Each library must support services demanded by its users and corresponding to their specific research goals
GRNTI 2022 Master Version. (Part 2. Updating and improving links and reference tools of the Rubricator)

The authors introduce the findings of their research on updating and improving the existing (2021) master version of the State Rubricator of Sci-tech Information (GRNTI) and preparing the master version 2022, in particular, updating and improving the Rubricator links and reference tools to these tools of GRNTI Master Version 2021. Four types of references are used to point to logical connections of subject headings supplementing the classification hierarchy fixed in the code structure of the GRNTI General Table, namely “see”, “see also”, “see cross reference”, “equivalent”, and Notes [in Russian]. The general statistics of updates and developments in all three GRNTI levels are presented in the Appendix A. Most changes are made to the “see also” reference, which evidences on the increased share of interdisciplinary scientific research in the thematic structure of the GRNTI 2022 Master Version. This level of generalization with logical grouping and concepts ordering, including polyhierarchichal connections, reflects the general trends in the modern scientific and technological knowledge. The reference tools of the GRNTI 2022 General Tables maintain distinction of aspects reflected in the subject headings with logical-semantic connections. This enables to refer documents to relevant headings when indexing.
Nonparametric estimation for a functional-circular regression model

Abstract Changes on temperature patterns, on a local scale, are perceived by individuals as the most direct indicators of global warming and climate change. As a specific example, for an Atlantic climate location, spring and fall seasons should present a mild transition between winter and summer, and summer and winter, respectively. By observing daily temperature curves along time, being each curve attached to a certain calendar day, a regression model for these variables (temperature curve as covariate and calendar day as response) would be useful for modeling their relation for a certain period. In addition, temperature changes could be assessed by prediction and observation comparisons in the long run. Such a model is presented and studied in this work, considering a nonparametric Nadaraya–Watson-type estimator for functional covariate and circular response. The asymptotic bias and variance of this estimator, as well as its asymptotic distribution are derived. Its finite sample performance is evaluated in a simulation study and the proposal is applied to investigate a real-data set concerning temperature curves.
Spam Classification Based on Machine Learning Algorithm

As technology continues to advance, email is used in almost every field. However, the dramatic increase in the number of spam emails has led to a growing need for accurate and powerful spam classifiers. Unfortunately, spam in its many forms is constantly being updated as the Internet evolves, and the challenge of fighting spam is enormous. With the emerging of deep learning methods, deep based algorithms are widely applied in most of the real-world scene and tasks. In this paper, we used CNN, RNN, LSTM and naïve Bayes models to implement spam filtering, and compared them based on information such as accuracy, model strengths and weaknesses. We test all the models on the public dataset and measure them by four metrics, including accuracy, precision, recall and F1-score. Finally, we conclude that the naïve bayes model achieves the best performance among all those methods, which can deal with the threat of spam efficiently.
Efficient Spam Classification Using Machine Learning Methods

With the development of the times, we receive more and more information every day, and email is an important carrier of receiving information in our daily life. In this context, some spam came out, including some advertisements and false information. Therefore, it is necessary to classify spam and receive it selectively. Machine learning algorithms can classify and identify spam with high efficiency, and in this paper, we compare six models and achieve a top result of 98.7%. Spam classification can filter out unwanted emails at the receiving end, effectively saving people's time and preventing malicious users from stealing personal information.
Review of Quantum Computer Development

In recent years, quantum computers have been attracting attention. Quantum computers have become a hot topic of research in today's scientific research. This paper first introduces the basic principles of quantum computers, including the storage principle and quantum logic gates, then introduces a quantum algorithm, then introduces the organization of quantum computers, and finally is a discussion that will evaluate the strengths and weaknesses of quantum computers, and discusses the physical implementation of quantum computers and the outlook on quantum computing.
Comparison of Quantum and Classical Algorithm in Searching a Number in a Database Case

Contemporarily, quantum computing is one of the hottest research fields. Many quantum algorithms are proposed in order to utilize the power of quantum computers. Grover’s searching algorithm is one of them. In this article, by comparing a classical searching algorithm and Grover’s algorithm in the problem of finding a number in a finite database, the advantages of the latter are discussed. The actual quantum circuit to solve the problem is built and run on both a simulator and a real quantum computer. According to the analysis, Grover’s algorithm provides speedup in a searching task compared to the classical algorithm. However, noises in today’s quantum devices make the result of the quantum algorithm unreliable. In searching for multiple numbers, Grover’s algorithm has its shortcomings. Nevertheless, noises in quantum computing need to be addressed in order to utilize the potential of quantum computers in solving difficult problems. These results shed light on guiding further exploration of quantum algorithms and quantum computing.
Fashion-MNIST Classification Based on CNN Image Recognition

Image classification is an important part of Artificial Intelligence (AI) and involves several tasks such as image normalization, image segmentation, feature extraction etc. Convolutional Neural Network (CNN) has been proved to be an effective network in image classification. According to this study, we had used a relatively small dataset named Fashion-MNIST (i.e. 70,000 images, 10 categories) to find out the model that can have a higher accuracy with limited training samples. We have trained three classic CNN models which are DenseNet121, MobileNet, ResNet 50 respectively. After that, we comprehensively measured the performance by several norms that comes from these three different models. Finally, we had a conclusion on which could be the most efficient one in this scenario based on the test results, in order to discovery which models are the most powerful one, and worth training. Human always deal with many types of images, so people need a powerful AI to help them to recognize and categorize these images. After this study, the DenseNet121 is the most powerful model in these three - DenseNet121, MobileNet, ResNet 50, the method to determine this result is that in the whole study we used a method called control variate method, we use the same amount of images, same amount of training times, then compared the final output of these three models, in the end we discovered that the DenseNet121 is the most powerful one.
Breast Cancer Prediction Based on the CNN Models

In modern society, the natural lifespan of an individual increased dramatically benefitting from advanced yet accurate methods of medical treatment. Though many diseases could be treated with a cure, the treatment of cancer has yet to be overcome. Related medical research has proven that the combination of accurate breast cancer diagnoses and treatments at an early stage could prevent the spread of cancer cells as it could increase a person's potential lifespan by a large margin. This research has conducted a comprehensive study on improving the efficiency of autonomous image recognition of breast cancer diagnosis using deep learning models. We use the most advanced CNN baseline models for image recognition, including VGG, ResNet, Efficient, etc. We also select two typical breast cancer datasets and tested the models on them to make our result more convincing. The final enhanced model of ResNet 101 can achieve a recognition rate of 89.98% for the benign and malignant samples.
Detecting Collectable Resources on Computer Game Based on the Neural Network

Image recognition and classification were put in used in many places in recent years thanks to the great progress in machine learning and neural networks. However, seldom evidence indicate applications of image recognition in computer games. Since games are gradually becoming part of peoples life, it is the time to put the focus on those who are unable to enjoy this entertainment. Nowadays computer games often include the element of collecting e.g. (materials or treasure). A program that could automatically mark those collectable objects would significantly improve the gaming experience for those who have disability in their eyes. In this work the game Destiny 2 was used for training and testing and training model chose was Yolov5. 1,500 of images were put into the data set and trained with 350 epochs. The model eventually achieved 0.82 in precision, 0.96 in recall and 0.91 in F1. It also gets the average mAP value of 0.93. These results can strongly prove that this model has the potential to be used in real scenarios.
Chinese Spam Classification based on Deep Learning

In the 21st century, it is an information age where individuals and organizations use computers to work and they commonly use email to chat with each other, share knowledge or their wishes or discuss business. However, in this context, spam is harassing our lives. Spam is a type of email that is delivered to lots of numbers of recipients without asking them for their wishes. Spam wastes our time reading useless messages and takes up our email addresses and network traffic. The problem of spam has become increasingly important and is growing over the years. The emergence of machine learning technology can simulate human intelligence. Use machine learning to identify the construct of pictures and words. Through this technology, this paper can classify the kind of different messages. Spam is very common nowadays; it can easily send lots of message to occupying a large amount of network bandwidth. Access such kind of messages, this article uses four models: Decision Tree, Support Vector Machine (SVM), Naive Bayes model, and Logistic Regression to identify spam to prevent it into our electronic mail.
E-mail Spam Classification using KNN and Naive Bayes

E-mail spam filtering is becoming a critical and concerned issue in network security recently, and multiple machine learning techniques have been applied to tackle such sort of classification problem. With the emerging of machine learning framework, most of the tasks has been changed via the effective machine learning algorithms with satisfying performance and high speed. However, the underlying performances of different algorithms under certain given circumstances still lack of an intuitive demonstration. Hence, this study mainly focuses on the performance of two widely-used algorithms (KNN and Naive Bayes) from metrics including accuracy and running time, comparing the unique advantage of each algorithm when classifying emails. The paper uses thousands of spam data to feed two algorithms and analyzes both results respectively, indicating that KNN classifier performs better when determining the spam messages while the opposite is true for the Naive Bayes classifier. Thus, designers can pick an appropriate algorithm easily when dealing with spam filter issues under a given dataset whose features and properties are known.
Data visualisation analysis of online novel websites

With the development of science and technology and the progress of the times, the online novel reading industry is also developing at a rapid pace, and online novel reading websites have also emerged. In order to make users better understand the current trend of popular novel genre popularity and give corresponding reference when users choose the novels to read. This study analyses novel creation data, novel creation genres, novel creation era, author writings and other data visualisation methods through bar charts, pie charts, radar charts, heat maps, word cloud charts and other data visualisation methods. Using web scraper, data on novel information is collected to ensure that the data is authentic and reliable. On the basis of cleaning the data using Python, the Echarts plugin is used to visualise the basic information of novels as well as the author's information to present the information of novels and readers in a more visual and vivid way. The study helps to obtain richer information about the novels and authors that one wishes to know, while making the readers' experience better and helping the website to operate and develop better.