Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Convolutional Neural Network Accelerator Based on FPGA

Convolutional neural network, as a kind of feed-forward neural network, has been widely used in image recognition, speech processing and other fields in recent years. In this paper, an FPGA-based CNN gas pedal is designed to solve the problem of slow running and high-power consumption of CNN on resource-constrained hardware. The design invokes multi-stage pipeline parallel processing technology to accelerate convolutional operations; quantifies network parameters from 32-bit floating-point to 8-bit fixed-point while guaranteeing CNN accuracy, and uses data multiplexing to reduce resource consumption. Experimental results show that the design is 10 times faster than the intel i7-8700 and consumes only 1% of the power of the RTX 2060 at 50MHz.
Research on sentiment analysis methods for text-oriented data

With the rapid development of information technology, the results obtained from sentiment analysis on a large number of speech information on these platforms can be used for comment classification, product analysis and recommendation, consumption forecast and other aspects of the network platform With the rapid development of information technology, the results obtained from sentiment analysis on a large number of speech information on these platforms can be used for comment classification, product analysis and recommendation, consumption forecast and other aspects of the network platform Sentiment analysis is a practical technique which has become one of the most active research fields in natural language processing. The traditional text sentiment analysis method consumes a lot of human resources, but the coverage of artificial extracted features is the traditional text sentiment analysis method consumes a lot of human resources, but the coverage of artificial extracted features is limited and the artificial irrational behavior will affect the correctness of the results, so the traditional method is not universal. With the development of deep learning, text pre-training language model and knowledge graph technology continue to develop. Aiming at the research of sentiment analysis methods for text data, we summarize the research background and domestic and foreign research status of sentiment analysis methods for text data, and explore the hot research content, key problems, commonly used experimental methods and technical lines of sentiment analysis methods for text text data.
Vehicle target detection algorithm based on yolov5

With the rapid advancement of automobile manufacturing technology and the intensification of competition among automobile brands, the development of autonomous driving has been pushed to the forefront of automobile development, which causes approximately $650 billion in losses due to traffic accidents worldwide every year. For in the complex vehicle target scene, because of its many vehicle targets, dense targets often exist between the occlusion, overlap, a variety of different weather reasons, rain, fog, sunny days, etc. resulting in obstruction of the field of view, and the vehicle camera often intercepted images mostly exist blurred, ghosting and other problems, making the vehicle target detection for detailed feature extraction requirements are high, the detection accuracy is often difficult to meet the requirements, proposed based on YOLOv5s vehicle target detection algorithm is proposed. ACmix attention mechanism is introduced to make the model achieve the purpose of expanding the target perception field, adaptively focusing on different target regions, and capturing more information features, etc. The test shows that the accuracy of the model increases by 1.1% on a subset of BDD100K data set after improvement. The accuracy of the improved model increased by 1.1% on the subset of BDD100K dataset and by 1.2% on the PASCAL VOC2007 general dataset.
Detection of floating objects in river based on improved HRNet

Human activities and other factors lead to a variety of floating objects in rivers, which seriously affect the quality of human life and natural ecological environment. It has become a widely concerned problem for residents and needs to be solved urgently. Relying on manual inspection to find floating objects is extremely low efficiency, urgent need of intelligent technology to detect and warn in time. To solve the problem of river floating object detection, we proposed an improved HRNet method for river floating object detection. Trunk network HRNet was used to replace the original AlexNet trunk network of Faster-RCNN to detect river floating object, which enhanced the feature extraction ability. A backbone combining DenseNet and HRNet is proposed to optimize the ability to extract details and improve the detection accuracy.
Unsupervise contrastive learning person re-identification Method based on Transfomer

Unsupervised learning person re-identification is a meaningful and challenging person retrieving problem resulting from its application on secure surveillance and missing of label. To handle the lack of person identities, CNN-based method has been proposed to use self-contrastive learning based on a memory dictionary. However, CNN's partial bias is not well addressed in the field of ReID. To overcome these limitations, we propose a transformer-based framework called (TransCC). Specifically, we take the ViT transformer encoder trained on ImageNet as the feature extraction network, which makes up for some disadvantage of the CNN model. In addition, to overcome the issue of cluster inconsistency in instance-based memory method, unique cluster vectors are used to represent clusters in Characteristic space stored in the memory dictionary. By updating the cluster centriods and calculating contrastive loss, the model can be optimized and learn person-identifying traits. The experiment results outperform that of most methods, which demonstrate the effectiveness of our approach on USL ReID.
Design and Realization of Surakarta Chess Game Program

At present, the research on Surakarta game is relatively less than other games. Aiming at this situation, a Surakarta chess game program is designed. This program implements basic algorithms such as board representation, move generation, game evaluation, and game tree search. The evaluation of the chess game takes into account the number of pieces, moving range, attacking range, attacking power of pieces, board value, arc value, etc. The game tree search algorithm uses the Alpha-Beta pruning algorithm. The whole program is fully functional and has a strong game level, which provides reference significance and reference value for related research. The program won the second prize at the national level in the computer game competition for college students in China, and the second prize at the provincial level in the computer game competition for college students in Liaoning Province, which verified the feasibility of the game program algorithm.
Video colorization with semantic preservation and consistency

Video colorization is greatly useful for reviving historical moments recorded by black-and-white media or sketch, portrait, and the semantic preservation within colorized frames is challenging. In the paper an text-driven video colorization method is proposed, which focuses on the hybrid deep network handling image object segmentation, foreground object colorization, background completion, text to image object linkage, and spatiotemporal preservation. The deliberately designed network is composed of two-layer LSTM and mask RCNN. The flexible training strategy avoids the overcost of separate training, while holds the advantage of text instruction, foreground object recognition and location. Experiments demonstrated that the quantitative criteria is better than the state-of-the-art method, and the user study shows that the color naturalness, saturation and richness is well preserved within the colorized video.
EDAD: Efficient Domain Adaptive Distillation by Integrating Domain Adaptation and Knowledge Distillation

In the field of natural language processing, a lot of progress has been made with the advent of Transformer having a self-attention mechanism. At the same time, the recently increasing model size causes difficulties in deploying the model for online serving that requires fast inference. To address this issue, one can employ model compression techniques when a target domain is coherent with the training corpus (i.e., a general domain) of pre-trained models such as BERT. However, the additional domain adaptation step is required along with model compression when we leverage such pre-trained models for special target domains such as medicine, law, finance, etc. In this paper, we propose an Efficient Domain Adaptive Distillation (EDAD) method to efficiently create a lightweight model capable of fast inference for a target domain by integrating knowledge distillation, which is one of the popular model compression methods, and domain adaptation processes. Experimental results demonstrate that EDAD can train a compact model for a target domain with much lower computational costs by integrating the two individual processes, adaptation and compression, into a single process and shows comparable performance with existing methods for named entity recognition (NER) tasks in the medical domain.
A Survey of Face Recognition Methods Based on Mask Region Restoration Algorithm

The complex and diverse background conditions, the variability of human face and the artificial deliberate transformation have all become the problems that traditional face recognition technology can't solve, and they can't really reflect the imperceptible advantages of face recognition. After wearing masks, the most important mouth and nose areas of human face are covered by these masks with different shapes and colors, and some facial features are hidden, and the key feature points that can be extracted are greatly reduced. In this paper, the existing mask region repair algorithms at home and abroad are classified and summarized, mainly including mask occlusion face recognition based on robust occlusion, mask occlusion face recognition based on sparse representation classification and mask occlusion face recognition based on neural network. It is pointed out that partial occlusion is one of the main difficulties. The main methods and shortcomings of face recognition based on mask region repair algorithm are systematically analyzed and summarized, and the main problems and possible research ways in the future are analyzed.
Scientific and technological achievements - intelligent management platform of intellectual property in universities

As many platforms on the market are of different quality, it is difficult to ensure the security of intellectual property rights, while many university campus websites lack the embedding of intellectual property management platform, which is not conducive to the transformation of scientific and technological achievements of intellectual property rights in universities. In the "Outline for the Construction of a Strong Intellectual Property State (2021 - 2035)"[1], it is clearly pointed out that the construction of a strong intellectual property state should be promoted in an integrated manner, the level of intellectual property creation, application, protection, management and service should be improved comprehensively, and the important role of intellectual property system in the construction of socialist modernization should be given full play. It is divided into three major sections: patent, trademark and copyright, to realize intelligent and safe management of customers, and at the same time provide professional IPR application services, laws and regulations publicity, application documents information, etc., which helps to accelerate the transformation of achievements of universities and enterprises. The purpose is to promote the transformation of scientific and technological achievements of universities, and it can also be applied to enterprises to maintain, manage and protect the intellectual property rights of rights holders for universities and enterprises [2]. In the future, the database of the platform can be expanded for deep learning-based IPR management to promote the integration of Internet application, management, transaction, and maintenance of IPR.
Research on Video Synopsis Based on Deep Learning Target Detection and Target Trajectory Optimization

In this era of rapid economic and technological development, monitoring is essential for security issues. However, it is also accompanied by the low efficiency of managing and viewing a large number of surveillance videos every day. Traditional methods are gradually unable to meet the requirements of application, and the video field requires algorithm technology that can better solve practical problems. Intelligent monitoring technology is gradually emerging and developing, based on theories such as computer vision, which can solve many monitoring video problems and greatly improve the efficiency of monitoring video work. Through in-depth exploration of existing video synopsis technology algorithms, this article proposes a new video synopsis method that can effectively detect and track videos, thereby greatly improving the efficiency of storage, transmission, and use of surveillance videos. Experiments have shown that this method can effectively concentrate surveillance videos, and compared with existing methods, it has a better synopsis ratio while ensuring the integrity of video information, effectively reducing collisions between targets, effectively reducing overlap between targets, and achieving good visual effects. Utilizing an improved deep learning object detection and multi object tracking algorithm with added attention mechanism to extract foreground moving targets in videos, and using a mixed Gaussian background modeling algorithm establish a background, laying a stable target foundation for subsequent video concentration. At the same time, design trajectory recombination optimization methods to ensure that the targets do not overlap as much as possible. Reasonably place the targets in the new condensed video sequence, determine the index of all targets in the new condensed video stream, and finally integrate each frame of target images into the background image according to the set index rules, ultimately obtaining the synopsis video.
OBJECTIVITY AND SUBJECTIVITY IN THE PROCESS OF CONDUCTING SCIENTIFIC RESEARCH: THE PROBLEM OF CORRELATION IN THE METHODOLOGY OF HISTORICAL COGNITION

The article studies the problem of correlation between the principle of objectivity as the basis of scientific knowledge and the diverse forms of human subjectivity of a scientist as a subject of a heuristic process. The regularity of changing the ratio of subjectivity and objectivity at various stages of a scientist's research search is revealed and justified – the influence of subjective factors in the process of scientific search decreases as the research progresses from its initial stages to the completion of the work, and in the final results and conclusions, scientific objectivity is achieved. The possibilities of achieving objectivity in the procedures of explanation, prediction, retelling, proof, evaluation, interpretation are determined. The features of special "historical" laws used in the methodology of historical cognition are revealed.
Development of academic libraries in Smolensk region in the early 20-th century (the case study of Smolensk first university

The author describes in detail the first years of the main scientific library in Smolensk Guberniya in the early 20-th century, namely Smolensk State University Library. The author was motivated by the absence of the complete picture of academic librarianship in the region; the issue is also insufficiently covered in the literature. The author applies the methods of comparative analysis and synthesis. The research is based on published and unpublished materials: documents in the State Archive of Smolensk Region; Rare Collection of Smolensk University Library; and scholarly papers. The author writes about the librarians who worked for the Library in 1918–1922, and its first director, privat-dozent I. E. Egorov. She also analyzes the history of the book collection development during the campaign of 1918–1921 in Smolensk Guberniya when the landed property and many manorial libraries were nationalized. She describes nationalized private collections in the Rare Collection of Smolensk University Library, and individual books from academic, public, scientific, estate, commercial, departmental, military and confessional libraries of prerevolutionary Smolensk. The role of Smolensk University Library as the richest book depository of the region and the value of its rare collections are emphasized. The vectors of the academic library activities of the above are explored. The research findings will contribute to the discipline “Library history” studied in the library higher schools and will be useful for modern academic libraries.
Smart Pension System Based on Big Data Mining Algorithm

With the increasingly prominent problem of aging in our country, it has become an urgent task for our society to establish and improve a good old-age care system. At present, a new type of community smart elderly care model is gradually emerging, and has been actively explored and improved. In order to meet the needs of community elderly care services, big data mining algorithms are used to process data, and the smart elderly care system is connected with mobile APP and computer server equipment. Open source WebGIS technologies such as OpenLayers/Cesium, PostgreSQL/PostGIS, GeoServer, etc. are used as core supports, combined with front-end And back-end development technologies such as Vue and SpringBoot, etc., developed a mobile smart pension app and a web smart pension system based on open source WebGIS. This system can be more in line with my country's current smart elderly care service system, and accelerate the construction process of my country's intelligent elderly care.
Problems and Improvement Measures of the Undergraduate Innovation and Entrepreneurship Training Program

In the Party's 20 National Congress reports, "innovation" appeared 55 times, covering politics, economy, culture, education and other aspects. The fifth part, "Implementing the strategy of rejuvenating the Country through science and Education and strengthening the talent support for modernization", mentions innovation for 21 times. It can be said that "innovation" has become the main melody and the strongest sound of today's times. College students' innovation and entrepreneurship training program is an important carrier to deepen the reform of innovation and entrepreneurship education in colleges and universities. In this paper, taking Zhejiang Yuexiu University of Foreign Languages as an example, the investigation and analysis found that there are some problems in the implementation of college students' innovation and entrepreneurship training plan, such as students' innovation and entrepreneurship awareness to be improved, project innovation to be enhanced, the professionalism of the tutor team to be improved, and the achievement evaluation system to be improved. In response to these problems, the author proposed corresponding suggestions and countermeasures. To constantly improve the "Big innovation" plan management system, improve the quality of the project.
Safe pedagogical communication: ethical and speech aspect

The article analyses the orientation and priorities of pedagogical communication in the conditions of humanistic paradigm of education. One of the main characteristics of pedagogical communication is the non-violent nature of pedagogical interaction. Frustration in interpersonal relations, emotional distress, insufficient level of professional self-awareness and reflection lead to the use of violent forms of communication in the pedagogical process. Pedagogical violence is a misplaced emphasis on the attitude of authority of the teacher towards the pupil. Also, it is accompanied by various forms of hostility, aggression and runs counter to the ethics and safety of pedagogical communication. The article proves the necessity of improvement of communicative competence and emotional culture of the teacher as necessary means of ensuring humanitarian adequate pedagogical relations. In addition, it describes the parameters of safe pedagogical communication: the building of trusting and dialogue relations with the pupil, insensitive behavior, the use of non-violent communication technology, clear formulas of speech pedagogical etiquette, technique of constructive critical remark, reduction of emotional tension.
On the performance and impact of research in storytelling by Russia scholars

The author analyzes the dynamics of scientometrical indicators (publication activity, citation and impact) of Russian research in storytelling in 2012–2021. Storytelling is seen as the marketing technology used to deliver information to the audience through stories. In recent years, storytelling has been intensively used in advertising, social media, corporate blogging, and in education. The lines of research of the greatest impact are identified, among them: storytelling as an efficient communication technology, digital storytelling in students’ projects, and storytelling method in corporate management. In accordance with the Hirsch index, the Russian scholars’ research activity in the field exceeds significantly the world minimum value of 16. The dynamics of the above indicators is revealed, including the rare occurrence of continuous growth in the number of publications by the end of the existent period; the organization which storytelling studies are extensively used by country’s other institutions, are identified, among them Nizhny Novgorod Kozma Minin Pedagogical University, Ural State Pedagogical University, RF Ministry for Civil Defense Academy of State Fire-fighting Service. The analysis was accomplished based on Russian Science Citation Index (RISC) database. The illustrations are included to visualize the research findings.
Research on Detection Algorithm of Safety helmet based on improved YOLOX

In the industrial production, construction and other fields, safety helmet as an important safety protective equipment, the wearing situation of workers personal safety and property safety is of great significance. Therefore, this paper proposes a safety helmet detection method based on the improved YOLOX algorithm. First, the 5000 pictures of safety helmet wearing at construction sites are labelled. The Squeeze and-Excitation module is introduced in the YOLOX network structure. The original Loss function is replaced with varifocal Loss. After experimental verification, compared with the original YOLOX target detection algorithm, our algorithm improves by 2.13 percentage points, enhances the model's focus on key areas and optimizes the model training effect, while the number of model parameters does not increase significantly. In conclusion, our algorithm has a wide range of application prospects and research value.
Social recommendation algorithm based on collaborative filter algorithms

For traditional collaborative filter recommendation algorithm technology, this paper combines the collaborative filtering recommendation algorithm with the community division technology of social networks, use the Louvain community to divide algorithms, divide the recommendation users to a community of similar users, and use the collaborative filter algorithm based on the user similarity formula within the community to recommend. In order to verify the effectiveness and accuracy of the algorithm in this paper, based on the introduction of the Douban dataset and the evaluation criteria used, a variety of comparative experiments are carried out on the Douban dataset with a variety of recommendation algorithms to verify the effectiveness of the proposed algorithm