Reviewerly/exHarmony huggingface dataset:
Research on application of machine learning algorithm in digital economy

This paper mainly uses support vector machine, random forest, logistic regression, and other machine learning algorithms to analyze the core data of listed companies, and then monitor their operating conditions and predict their bankruptcy probability. At the same time, the prediction effects of the three machine learning methods are evaluated by confusion matrix, ROC curve and other methods and indicators, and the best effect of the method is found. By predicting the bankruptcy probability of listed companies this study further supplements and improves the theory of machine learning algorithm on economic quantitative analysis.
Communicative Space of Didactics in the Context of the Implementation of Information and Communication Technologies

Communicative space is a set of various types of communication (external, internal) and interhuman relations (trusting, negative), which are established between the subjects of the communicative space in the process of their interaction. The communicative space of didactics is understood as a set of conditions and mean of professional training of a communicative personality in the process of communication. Communicative space is considered as a system of interrelated educational and educational situations aimed at the formation of communicative competence, including speech activity and speech behavior. Pedagogical (teaching) communication involves the establishment of interpersonal contact, in which a dialogue arises. Various forms of dialogue communication are used as a means of teaching a person, an active participant in joint activities, as well as a means of enhancing the activities of students in the process of mastering certain educational content. The dialogical approach in the communicative space is the basis for the teacher’s preparation for competent interaction in the educational environment. Modern teaching tools are based not only on interaction, but also on the active use of various forms of e-learning, including information and communication technologies (ICT). Particular attention is paid to the characteristics of information and communication technologies in the areas of methodological, educational purpose. The use of new technical means in the educational process adjusts the goals and form of vocational training.
Collaborative Writing as an Effective Tool in Teaching Foreign Language Oriented to the Vocational Education of Engineering Students

Education for sustainable development requires the search for advanced teaching and learning methods that motivate and empower students to acquire the knowledge and skills to shape their future. Cooperative learning is one of the most effective methods in terms of involving learners working together on diverse activities. Teaching professionally-oriented foreign languages at engineering universities makes students move toward better writing by putting higher requirements on the writing skills needed for their future employment and career advancement. Thus, the presented paper is aimed at analyzing the theoretical aspects and practical applications of collaborative writing as a powerful tool for creating a more effective learning environment. The paper defines the concept, key principles, stages of implementation, as well as advantages and potential of collaborative writing. Besides, it highlights the opportunities and challenges of digital platforms in providing the technology of collaborative writing. It also contains the results of a survey that confirmed the effectiveness of collaborative writing in teaching a professionally-oriented foreign language. The findings of our study might be useful for both education theorists and practitioners. To further our research, we are planning to analyze the use of collaborative writing technology, expand its possibilities, and enhance the efficiency of its application.
Project-Oriented Foreign Language Training of Technical Students

The article presents the experience of using a project-oriented teaching a foreign language model in professionally aimed training of technical students. The analysis of the theorists’ research studying the project method and applying it in practice helped to justify the need to use the project method in foreign language classes at the Don State Technical University, to prove its effectiveness in the formation of students’ cognitive independence. The authors conducted an experiment, for the implementation of which special modified Ch. D. Spielberger’s questionnaires were compiled. During the experiment, the method of a professionally oriented project was introduced into the educational module of the discipline “Foreign Language”. The stages of work on the project are described, the roles of participants are indicated. The results obtained are analyzed; the strengths and weaknesses of the creative product are determined; at the stage of reflection, an emotional and psychological state was revealed. Conclusions are drawn that teaching in the format of project activity affects the cognitive interest in the field of foreign language learning; information obtained during the preparation of the project is easier to digest, acquires significance, and emotional involvement contributes to improving the level of language proficiency. The article will be useful for teachers of a foreign language of higher education and teachers of additional education.
Development of the Bachelor’s Major “Applied Mechanics” at T-University

In order to further develop and improve the university program for the training of mechanical engineers, the paper considers the fundamental changes in the curriculum for the training of bachelors in the program “Applied Mechanics”, profile “Software Systems of Computer Engineering”. For the training of specialists in the environment of the T-university, a new major developed for this educational program is described.
Production of carbon from invasive weeds, surface modification, and its application

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Manish S. Khodwe, Deepak Tiwari, Srinu Nagireddi; Production of carbon from invasive weeds, surface modification, and its application. AIP Conf. Proc. 3 February 2023; 2558 (1): 020029. https://doi.org/10.1063/5.0120043 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A GAN-based small target detection model

Small objects have low content in the image, insignificant features, and are easily disturbed by noise, so that fewer features can be used for target detection. However, the Faster R-CNN object detection model based on deep convolutional neural network undergoes multiple pooling operations during feature extraction, which makes it more difficult to extract the features of small objects effectively, which is unfavorable for the detection of small objects. Aiming at this problem, this paper proposes a Faster R-CNN-based small object detection model that uses GAN to enhance the small object feature expression. First, based on the strong ability of deep feature extraction of Resnet152, Resnet152 is used to replace VGG16 in the original Faster R-CNN; secondly, the GAN model is trained by using appropriate high-resolution object features as the supervision signal of the generation network; finally, set the threshold of small object, so that the object features larger than the threshold directly enter the detection network and reduce the complexity of the model.
Automatic detection of fastener safety wire twisting direction based on machine vision

Safety wire status detection is of great significance to aircraft operation safety. To address the problems of low efficiency and leakage in the current manual-led safety wire twisting direction inspection, we propose a machine vision-based safety wire twisting direction detection method for aviation fasteners. Firstly, the Yolov4-MSC objective detection algorithm is proposed for fastener and safety wire region localization. After segmentation of the region localization, we pinpoint the precise location of the fastener by the gradient Hough transform method, the safety wire braiding is detected using the Maximally Stable Extremal Regions (MSER) with Non-Maximum Suppression (NMS), and the safety wire centerline location is fitted using the Random Sample Consensus (RANSAC) algorithm. Lastly, the safety wire twisting direction detection is obtained by the position relationship between the safety wire centerline and the fastener. The experimental results show that the proposed safety wire twisting direction method is effective, has high accuracy and robustness, and can achieve fastener and safety wire location and safety wire twisting direction detection in complex aircraft maintenance scenarios.
Learning-based client selection for multiple federated learning services with constrained monetary budgets

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance.
Genentech, Inc. v. Sandoz, Inc.

Biotechnology Law ReportVol. 42, No. 1 Case in PointGenentech, Inc. v. Sandoz, Inc.United States Court of Appeals of the Federal Circuit, 2022 No. 2022-1595, 2022 WL 17839055Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:15 Feb 2023https://doi.org/10.1089/blr.2023.29297.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 1Feb 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Genentech, Inc. v. Sandoz, Inc..Biotechnology Law Report.Feb 2023.39-47.http://doi.org/10.1089/blr.2023.29297.cipPublished in Volume: 42 Issue 1: February 15, 2023Online Ahead of Print:February 3, 2023 TopicsGene therapy PDF download
Self-supervised monocular depth estimation with coordinate attention

Predicting depth information from a single image has recently become an important research topic in computer vision. In particular, the self-supervised strategy for learning the depth is more attractive because it is not necessary to label any ground truth information. Under the framework of self-supervised learning we propose a CA-depth network to improve the accuracy of a single image depth estimation. We added the attention mechanism to the monocular depth estimation network to address the issues of observable artifacts and inaccurate prediction geometry in monocular depth estimation images. The spatial position information in the high-dimensional feature map is used to pay attention to the essential features, and to weaken the artifact phenomenon in the depth prediction map. We used Resnet as the encoder to extract the input image's feature map, the coordinate attention mechanism to realize the optimal allocation of convolution feature map weight, and the decoding network structure to predict the depth. Experimental results on public datasets show that the depth prediction accuracy of the CA-depth network is higher than the state-of-art methods.
Fake face detection by wavelet higher order statistics

With the development of deep neural networks, the ability to generate fake faces has improved significantly. Although many fake generation algorithms appear to generate real faces, they do show artefacts in some areas that are not visible to the naked eye. In this paper, we use the feature extraction algorithm in image steganography to extract features, and at the same time, we use a simple classifier type classification. Compared with previous systems that require a large amount of labeled data input, our method achieves good results with only a small number of annotated training samples.
Byzantine fault tolerant consensus mechanism based on multi-group voting

This paper fully studies PBFT and its improved consensus mechanism. PBFT, as a classic consensus mechanism in the blockchain to solve the Byzantine fault tolerance problem, has a fault tolerance rate of 33%, but it has problems such as lack of dynamics, no scalability, and the consensus efficiency decreases with the increase of the number of nodes. Although the improved consensus mechanism of PBFT improves the existing problems of PBFT to a certain extent, it still has other problems such as high energy consumption and delay, and it cannot be put into use very well. Based on the above problems, this paper proposes a Byzantine fault-tolerant consensus mechanism based on a multi-group voting mechanism. The biggest problem with PBFT is that it is not dynamic. When the nodes in the system change dynamically, it will crash, and it must be restarted to run. In response to this problem, this improved method introduces a multi-group voting mechanism, which enhances the fault tolerance of the consensus mechanism by grouping nodes in the network and generating production nodes through two-stage voting. At the same time, in the production node consensus stage, the production node replacement protocol is used to improve the security of the consensus mechanism and make the improved PBFT dynamic. After completing the improved design of the consensus mechanism, compared with the PBFT consensus mechanism, it is superior to PBFT in terms of energy consumption, delay, fault tolerance, etc.
A CNN-LSTM-based model for fashion image aesthetic captioning

We propose a new task, how to describe apparel in an aesthetic way, which called fashion image aesthetic captioning. It can be beneficial to the E-commerce since there are tons of clothes needed captioned to capture customers’ eyes. It will also help people understand fashion better. We adopt the architecture of encoder-decoder as our baseline. We introduce two classifiers - color harmony classifier pretrained on AVA dataset as well as clothes type classifier to enable encoder to extract more correct features from clothes images. As for decoder we use LSTM with attention mechanism to generate sentences. Additionally, we build a new dataset containing 79,105 fashion images with aesthetic description and attributes. The experiment on the dataset shows great results of our model.
Aerial image detection of drone based on improved YOLOv5

Drones have been used in many practical applications,,such as agriculture, aerial photography and surveillance. Therefore, it is very difficult for machines to automatically understand the visual data collected by drones, which makes the connection between computer vision and drones become inseparable. However, the identification of targets from aerial images is faced with the difficulty that the target to be detected is usually too small, too dense, and the relative size is not fixed relative to the image, and the relative motion of drone and traffic leads to the blurred target. To address these challenges, this paper proposes an improved YOLOv5 network model. Based on YOLOv5, we integrate the convolution block attention model ( CBAM ) and the attention mechanism of Swin Transformer to enable it to effectively focus on the attention area in dense small object scene and reduce the calculation amount. We also adopt the BiFPN structure for the structure of the neck network, which can obtain more effective feature information and reduce some unnecessary connections. In addition, we also add an additional detector to detect small objects. To further improve our YOLOv5, we adopt the data enhancement strategy. Extensive experiments on the VisDrone 2019 dataset show that the The AP result of the improved YOLOv5 was 32.83 %. Compared with the baseline model ( YOLOv5 ), YOLOv5 increased by about 6.5 %, indicating that the improved model was effective.
Robotic arm dexterous grasping system based on RGBD

Aiming at the problem that service robots have poor dexterity in grasping objects with arbitrary postures in the home environment, a dexterous grasping method adapted to objects with arbitrary postures is proposed. First, the YOLACT instance segmentation network is used to recognize and segment the target object, and the segmented target object is registered with the depth image to obtain the target point cloud. Then the target point cloud and the template in the template library are used to estimate the accurate pose of the target object by using the ICP algorithm. Finally, according to the obtained object pose, the grasping pose of the robotic arm is standardized to achieve dexterous grasping of the object. The experimental test shows that the grasping method proposed in this paper has a high total success rate of grasping objects in different postures and the grasping method is beneficial to improve the dexterity of home service robots for grasping objects, and is significant for the development of home service robots.
Graph convolutional neural network for individual consumption behavior analysis

It is a research topic of great significance to analyze individual consumption behavior and make targeted recommendations. In this paper, we propose to use graph convolutional neural network to analyze individual consumption behavior. Compared with the previous methods, our proposed method has great advantages in speed, accuracy and model size.
Fuzzy image reconstruction algorithm based on multi-scale UNet-attention neural network

A multi-scale UNet-attention neural network image reconstruction method is presented to increase the reconstruction quality of degraded images. To improve the reconstruction effect, a "graph-to-graph" U-Net image reconstruction network is built first. Second, a multi-scale input with an image pyramid structure is developed to extract more scale image information while retaining image details. In addition, the attention mechanism is combined to select the important information to obtain the reconstructed image with better visual quality. The experimental results show that the algorithm can recover the image better from the visual effect, and the reconstruction effect is significantly improved compared with the traditional method by using the SSIM evaluation index, which is 0.6 in value than the improved Wiener filtering algorithm and 3dB higher than the UNet structure alone in terms of PSNR index.
Research on aircraft descent speed measurement based on camera array

The descending speed of the aircraft is fast and the time is short. How to measure the instant descending speed has become a research hotspot. At the moment of descent, the on-board sensors and other equipment often have intermittent signals at the moment of landing, resulting in data gaps, which cannot meet the needs of actual scientific research. Based on this, a set of high-speed camera arrays is designed in this paper, and the camera angles are overlapped by cross-layout. The camera hardware is programmed to grant GPS time uniformly, which unifies the camera time. Then, the method of multi-eye vision measurement is used to measure the descending speed of the aircraft, and the simulation verification is carried out, and good results are obtained. Finally, the error is evaluated, and the measurement speed accuracy can meet the actual use, which shows that the establishment of the camera array measurement system can quickly and effectively solve the problem of aircraft descent speed measurement.
Cross-domain named entity recognition based on embedding hierarchical vector into multi-cell compositional LSTM

For unsupervised cross-domain named entity recognition, the texts of different domains have different features, and there are also a large number of domain-related specific vocabularies, resulting in some specific words of the target domain are rarely learned in source domain, or have different meanings. In order to solve above problems, we are the first to propose that embedding hierarchical vector representation into the multi-cell compositional LSTM-CRF model, sentence vectors are added on the basis of the character-word vector to form the character-word-sentence hierarchical vector representation. Based on the different contributions of different words to sentences, the model constructs sentence vectors by using label attention mechanism, so that sentence vectors can use more comprehensive information to infer the features of domain-related specific vocabularies, reduce its interference to model understanding. The multi-cell compositional LSTM model encodes various entities and uses the relationship between words and entities to transfer cross-domain knowledge from the word sequence level to the entity sequence level. Finally, after modifying the boundary of the label sequence through the CRF layer, the final result is obtained. In addition to the main task of named entity recognition, the model also uses LM (Language Modeling, LM) task to assist in learning the domain features of the target domain. The experimental results show that the F1 value of the model proposed in this paper has been greatly improved in the different cross-domain datasets.
Event extraction enhanced by fusing trigger feature

Event extraction is a key research direction in the field of information extraction. In order to improve the effect of event extraction and solve the problem that the general event extraction method cannot make full use of the text feature information, an event extraction method integrating trigger word features is proposed. By building a remote trigger thesaurus, we can provide additional feature information for the event type classification model, enhance the ability of discovering event trigger words. Then the event arguments extraction model integrates the event type and trigger distance features to improve the representation learning ability. Finally, connecting the event type classification model and the event arguments extraction model in series to complete event extraction. Experiments are carried out on the DuEE dataset and the result shows that our model has more outstanding performance than other models.