Reviewerly/exHarmony huggingface dataset:
NLP BASED TEXT SUMMARIZATION USING BART MODEL

Text summarization plays a major role which can be used to summarize articles, papers, documents, etc. It greatly helps the students, business executives, librarian, teachers and many people in different profiles to quickly get the gist of research papers, latest news, trends in industry, summarize books, articles, etc. Summarizing provides a concise overview of a document which helps us to make better decisions and communicate information more effectively. In this project we are going to implement an nlp based text summarization using bart model and finally deployed using django framework. Bart hugging face transformer model is one of the nlp models. Its input and output are in the form of sequence. The high- dimensional representation of the input is learnt by the encoder and then mapped to the decoder. The bart model is first pre- trained on a big text (like book corpus or wikipedia). Pretraining ensures that the model "understands the language" and has a solid foundation from which to learn and how to carry out further tasks. Since it will determine how successfully the model can be trained for tasks like text classification or text summarization, the model's capacity to understand language is more effective. The pre-trained weights and weights in the bart model are fine-tuned on question answering, text summarization, sequence classification, etc. Therefore, using bart model we can able to generate text in both extractive and abstractive way, by which we can get the simpler summarization with the important sentences and also more fluent and accurate summarization by understanding the meaning of the text. Finally, to provide a user-friendly interface, by which the users can easily use the text summarization, the project will be deployed using django framework. It allows us to easily build web applications. Key Words: BART, NLP, summarization, Wikipedia, pretrained
Remote Object Taking and Movement in Augment Reality

In this article, we first use data gloves and AR glasses to build a basic interactive environment for augmented reality applications. We use four commonly used gestures: grabbing, putting, single-finger pointing, and two-finger pointing. Based on these gestures, we implemented grabbing and placing operations for general virtual objects and selecting, grabbing, moving, rotating, and placing operations for remote objects in the AR scene. Then we compared the remote interaction gestures we designed with the traditional moving and grabbing method. User experiments show that the proposed interactive gestures have a good naturalness. Some users with little experience can operate virtual objects in AR smoothly, and the operation efficiency is relatively high. Moreover, the differences between the two remote objects are compared and analyzed.
Shape Retention Based 3D Point Cloud Reconstruction from a Single Image

The reconstruction of 3D point clouds has always been a difficult problem, and it is a great challenge to highlight their edge characteristics for the constructed point clouds. Existing point cloud reconstruction methods ignore the problem of blurred point cloud edges. This paper introduces the shape retention point cloud reconstruction (SRPCR), which is a multi-stage generative network to enhance 3D reconstruction results from 2D images. The Fusion of the image features and edge information is utilized to generate a more accurate sparse point cloud. The high-resolution dense point clouds are generated by adding channel attention mechanisms to the point cloud generation adversarial network, and more detailed boundary information is achieved from a single image. The experiments demonstrate SRPCR outperforms existing point cloud reconstruction methods on the ShapeNet dataset.
A Face Recognition Conference Attendance System Based on Deep Learning

Face recognition based on deep learning is an important application of artificial intelligence. For meeting check-in and other scenes that need to take into account both recognition accuracy and response time. The conference sign in system developed in this paper is based on clmtrackr library to realize pure front-end live detection, and based on RetinaFace algorithm to realize face position detection. The optimized FaceNet algorithm realizes the extraction of 128 dimensional feature vectors of the face. In view of the problem that the original Inception of FaceNet is easy to cause the feature map to be too thick, the improved MobilenetV1 model is used to improve the speed of face recognition. The data set test results in LFW show that the average recognition speed is increased to 0.003 seconds per frame, which is 0.001 milliseconds less than that of the ordinary model per frame, and the performance is improved by 25.0%. At the same time, the accuracy can reach 97.3%.
A Convolutional Knowledge Tracing Method based on Graph Embedding

With online education rapidly developing, it is a significant issue to evaluate students’ grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students’ answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance.
Electrical Fittings Inspection Based on Improved Unet with Generative Adversarial Network and Attention Mechanism

Inspecting electric power fittings in transmission lines is a complex task prone to errors due to the challenges posed by the variable shooting angles and the complex background in the images. Additionally, there exists the issue of mutual occlusion that occurs between fittings and imbalanced data sample categories. These challenges can result in false detections and leaks in the fittings. Therefore, a fittings detection method based on an improved U-shaped network (Unet) with a fast generative adversarial network and attention mechanism (FA-Unet) is proposed in this paper to address the aforementioned challenges. Firstly, virtual fittings samples are produced by the generative adversarial network to address the issue of unequal sample size in the dataset. Next, a mixed attention mechanism that combines self-attention and convolution (ACmix) is adopted to improve the model's ability to extract fittings features in complex backgrounds. Finally, an enhanced mix loss function is developed to solve the problem of unbalanced positive and negative samples in the fitting dataset. The experimental results demonstrate that the FA-Unet has achieved a commendable detection performance. The approach considers the network's performance and improves the detection effect compared to the original Unet.
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

Multi-object tracking (MOT) is a cornerstone capability of any robotic system. Tracking quality is largely dependent on the quality of input detections. In many applications, such as autonomous driving, it is preferable to over-detect objects to avoid catastrophic outcomes due to missed detections. As a result, current state-of-the-art 3D detectors produce high rates of false-positives to ensure a low number of false-negatives. This can negatively affect tracking by making data association and track lifecycle management more challenging. Additionally, occasional false-negative detections due to difficult scenarios like occlusions can harm tracking performance. To address these issues in a unified framework, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShaSTA</i> which learns shape and spatio-temporal affinities between tracks and detections in consecutive frames. The affinity is a probabilistic matching that leads to robust data association, track lifecycle management, false-positive elimination, false-negative propagation, and sequential track confidence refinement. We offer the first self-contained framework that addresses all aspects of the 3D MOT problem. We quantitatively evaluate ShaSTA on the nuScenes tracking benchmark with 5 metrics, including the most common tracking accuracy metric called AMOTA, to demonstrate how ShaSTA may impact the ultimate goal of an autonomous mobile agent. ShaSTA achieves 1st place amongst LiDAR-only trackers that use CenterPoint detections. The open-source code for reproducing and extending our work can be found <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tsadja/ShaSTA</uri> .
A multimodal fusion human activity recognition method based on CNN and Gramian angular field

In order to solve the problems of complex and time-consuming traditional manual feature engineering methods in the field of human activity recognition, incomplete grasp of local information by a single recurrent neural network (RNN) and lack of utilization of temporal information by a single convolutional neural network (CNN), this paper uses the Gramian Angular Field (GAF) algorithm to convert inertial sensor data into images and designs a multimodal fusion based on CBAM-CNN-Attention method, which can effectively improve the human activity recognition accuracy. Firstly, the time series and image features are extracted using one-dimensional convolutional kernel and two-dimensional convolutional kernel, respectively. Secondly, the most important features are obtained and optimized using the attention mechanism. Finally, the recognition results are classified using a softmax classifier. The experimental results on the UCI-HAR dataset show that our proposed method has superior performance to other methods that use only data collected by accelerometers.
Optical and SAR remote sensing image ship detection based on attention mechanism

The application of deep learning-based methods in ship detection remains challenging due to the small size of the target of interest and the interference of complex sea surface background. Meanwhile, existing ship detection methods tend to experiment on a single image type, but rarely verify the applicability of their methods to multi-source sensor images. Therefore, this paper proposes a multi-scale deformable ship detector (MDSD) method based on the YOLOv3 framework for ship detection in optical and synthetic aperture radar (SAR) images. Since various information in the complex sea surface background can interfere with target detection, the detection algorithm designed in this paper includes a new multi-scale deformable attention module (MDAM), which can be added to the FPN part of the YOLOv3 model to enable the network to highlight target features while suppressing background features. The proposed MDAM generates attention maps from feature maps with multiscale deformable sensory fields, which can be better adapted to ship objects of various shapes and sizes. Compared with other models, MDSD has the highest detection accuracy on both optical image dataset (LEVIR) and SAR image dataset (SSDD). Also, MDSD is robust, which is validated on mixed datasets containing optical and SAR images.
Unsupervised domain adaptation based on label optimization and optimal transport

Deep unsupervised domain adaptation (UDA) methods are widely used in the computer vision. However, such methods are usually negatively affected by inaccurate pseudo-label, which impairs model performance. This paper proposes a new UDA framework for optimizing pseudo-label by using the mean teacher model (LOOT). LOOT first constructs a teacher model using the source domain data to provide robust pseudo-label for subsequent training. A threshold strategy is also proposed to drive the model to learn to a more accurate output. Second, an optimal transport method using the JSD regular is proposed to align the global distribution of the source and target domains. In addition, the model is equipped with clustering objectives to enable discriminative domain invariant feature learning. The combined experiments show that LOOT achieves state-of-the-art result in the benchmark datasets.
The main stages of ore formation of the Svetloe epithermal gold deposit (Khabarovsk Region) based on the study of the mineral composition of metasomatic rocks and ores

The study of the spatiotemporal relationships of metasomatites and ores of various zones of the epithermal Au-Ag deposit Svetloe (Khabarovsk Region) using a traditional mineralogical and petrographic approach made it possible to establish several stages of ore formation confined to two paleovolcanic structures of the central type. The early stage corresponds to the moderately acidic (IS) type of mineralization, the second stage of ore formation corresponds to the acidic sulfate (HS) type.Gold mineralization associated with the early stage of ore formation is represented by two main types: Au-Ag-Te and Au-Ag. The late stage of mineralization is characterized by the predominance of redeposited high-grade gold, as well as the presence of other native elements: tellurium, selenium, sulfur, and bismuth.
Research on multivariate time series prediction method based on missing data imputation

In multivariate time series (MTS), missing data will lead to inaccurate predictions. In this paper, we propose a multivariate time series forecasting model based on bidirectional gated recurrent neural networks (RNN) combining self-attention with residual connections, abbreviated as BGRRSA. At the same time, the model also uses univariate time series prediction to extract historical information from the target series. Finally, the outputs of the multi-level self-attention mechanism and univariate time series prediction are weighted and summed to obtain the model's final prediction. Experiments were conducted to compare and analyze different missing values imputation methods and different time series prediction methods on two datasets, verifying the effectiveness and accuracy of the model.
Image enhancement algorithm for crystallization state detection of penicillin salt solution

The crystalline state detection of penicillin salt solution is performed on the basis of the acquired images of penicillin salt solution, and in order to improve the accuracy of subsequent detection, an image enhancement algorithm for crystalline state detection of penicillin salt solution is proposed for the problem of poor quality of penicillin salt solution images. Firstly, in order to remove the disturbing factors in the penicillin solution image and extract its target solution region, an automatic image segmentation algorithm based on OTSU algorithm and mathematical morphological processing is proposed; secondly, using the characteristics of three independent channels in HSV space, a linear grayscale transformation and mean filtering method is designed for the H component to realize the color adjustment of the target solution region, and the V component is used to enhance the image brightness by Finally, the enhanced target solution region is obtained by combining the components, and the final enhanced image is obtained by spatial transformation and mapping. The experimental results show that after the algorithm processing, the accuracy of crystallization detection of penicillin solution in uncrystallized state and crystallized state reaches 99.32% and 99.40% respectively, which is 30.72% and 9.22% higher than the accuracy before enhancement, which can effectively improve the accuracy of crystallization detection of penicillin solution.
M2F2-RCNN: Multi-functional faster RCNN based on multi-scale feature fusion for region search in remote sensing images

In order to realize fast and accurate search of sensitive regions in remote sensing images, we propose a multi-functional faster RCNN based on multi-scale feature fusion model for region search. The feature extraction network is based on ResNet50 and the dilated residual blocks are utilized for multi-layer and multi-scale feature fusion. We add a path aggregation network with a convolution block attention module (CBAM) attention mechanism in the backbone network to improve the efficiency of feature extraction. Then, the extracted feature map is processed, and RoIAlign is used to improve the pooling operation of regions of interest and it can improve the calculation speed. In the classification stage, an improved nonmaximum suppression is used to improve the classification accuracy of the sensitive region. Finally, we conduct cross validation experiments on Google Earth dataset and the DOTA dataset. Meanwhile, the comparison experiments with the state -of the- art methods also prove the high efficiency of the proposed method in region search ability.
Beyond the software: The importance of personal communication to ensure access in complex ILL and other borrowing transactions

This paper focuses on the ILL aspects of a larger project investigating the continued availability of items identified in a 1991 bibliography (Dillard).For this stage of the research, the particular focus was on obtaining various theses and dissertations, as well as books and sheet music items.As Lowry (2006) noted, especially in the humanities and social sciences, the majority of dissertation and thesis research has traditionally not resulted in further publication, with only about 10% of this work being republished in other forms.This means that the original works are the only source of this research, and therefore there is a need for continued access.While aggregated databases such as ProQuest Dissertation and Theses Global provide instant full text access in many cases, other items are only available for separate purchase through the database, by borrowing through ILL channels, for download from individual institutional repositories, or for viewing in the library of the degree-granting institution (Smith, 2023).There are also issues in identifying more recent dissertation and theses research as there is no one universal source, but rather a proliferation of different directories and research databases where they may be listed, or they may only be listed within a single institutional repository (Center for Research Libraries, n.d.).Despite the many difficulties encountered, one theme emerged from these retrieval endeavors: the importance of personal communication in overcoming barriers and ensuring access to materials.This paper highlights the different ways in which establishing communication promoted understanding firstly between the ILL librarian and the researcher, and then at each stage of the borrowing process, resulting in mostly successful transactions.Communication was able to overcome barriers of the borrowing institution's ILL policy that previously prevented successful transactions; it enabled navigation through complex and multi-stage digitization requests involving international universities and national libraries; it enabled direct digitization and electronic delivery of items at no cost to the borrower; and it enabled identification and retrieval of items from institutional repositories.
Accuracy comparison between decision tree and naive Bayes algorithms in large discrete dataset with incompletely independent attributes

Decision tree and naive bayes algorithms as classification algorithms are largely implemented in data mining. They are widely used in various data types which present accuracy differences among each type. In this essay, principles of two algorithms are introduced, and a specific data type, a large discrete dataset with incompletely independent attributes is used as a model to train those two algorithms. Then, two algorithms are tested, and confusion matrix method is used to calculate the accuracy of algorithms. Finally, two algorithms are compared, and it is concluded that accuracy of decision trees is better with large discrete datasets and attribute correlation.
Impacto del Programa Aprendo en Casa en estudiantes del nivel inicial en el distrito de Corrales - Perú: Un análisis desde la perspectiva parental

The objective of the study carried out was to discuss the effectiveness of the Aprendo en Casa Program in the students of the initial level of the Corrales district from the perspective of the parent, this work was carried out during the COVID-19 pandemic, in which education was faced great challenges around the world. Initial education teachers have had to quickly adapt to remote classes to ensure continuity of learning for their students. In this context, the Aprendo en Casa Program has been implemented as a fundamental strategy to facilitate distance education and ensure that preschool-age children can continue to develop their skills and knowledge from home. The methodology used was non-experimental, quantitative, of a simple descriptive type. Data collection was carried out by applying a Likert-type questionnaire to 44 parents of children enrolled in the Santa Rosa de Lima educational institution in the Corrales district. The results obtained indicate that the Aprendo en Casa Program, according to the evaluation of the parents, obtained efciency levels such as Excellent in the objectives dimension with 55%, in the activity dimension with 80% and in the material dimension educational with 40%. It was concluded that the I learn at home program is an excellent and effective tool for remote classes.
GN-CycleGAN: artistic style transfer with gradient normalization and cycle generative adversarial networks

Image style transfer is a technique for generating images with the help of computers. It can transfer the style features of image B to image A to generate image A with style B, which can not only meet the individual needs of users, but also meet the needs of users. The problem of insufficient image data for a certain style. In recent years, a series of models such as CycleGAN have achieved great success in the field of domain style transfer, but their training is unstable, and the problems of blurred, unstable, and inefficient generated images that are prone to mode collapse have not been solved, this paper proposes the GN-CycleGAN model for this problem. In this paper, a GN-CycleGAN model is proposed to solve this problem. By replacing the instance normalization in the discriminator with the gradient normalization, the discriminator space can be smoother without affecting the performance of the discriminator, so as to overcome the training instability of CycleGAN caused by steep gradient space. The experimental results show that, compared with CycleGAN, the loss function of GN-CycleGAN model can converge quickly under the same training time,, and it performs better on the monet2photo dataset. The generated images are in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity) It is better than CycleGAN in two indicators.