Reviewerly/exHarmony huggingface dataset:
Visual Analytics Technologies for Situation Awareness

Visual analytics technologies are very important for mining information and knowledge from big data. Its goal is to generate images that people can obtain information intuitively. A good visual analytics method is an important way to promote human-machine interaction, and it is also an important technology in the current era of big data and artificial intelligence. Situation awareness is a typical application of mining information from data. By reviewing the role of visual analytics on human cognition process, it is proposed that visual analytics also plays an important role in situation awareness. Combining the visual analytics technologies and situation awareness process, three models are studied in this paper. Starting from the information transformation model of situational awareness, how the situation information are transformed is illustrated. Then the situation awareness model with visual analytics is proposed, in which the process flow with analytics models and visualization technologies is illustrated to enhance human's situation awareness. Finally, the processing flow from the perspective of data state is proposed, which provides a basis for the development of better human-machine interaction situation awareness methods.
One‐stage self‐distillation guided knowledge transfer for long‐tailed visual recognition

Deep learning has achieved remarkable progress for visual recognition on balanced data sets but still performs poorly on real-world long-tailed data distribution. The existing methods mainly decouple the problem into the two-stage decoupling training, that is, representation learning and classifier training, or multistage training based on knowledge distillation, thus resulting in huge training steps and extra computation cost. In this paper, we propose a conceptually simple yet effective One-stage Long-tailed Self-Distillation framework, called OLSD, which simultaneously takes representation learning and classifier training into one-stage training. For representation learning, we take two different sampling distributions and mixup them to input them into two branches, where the collaborative consistency loss is introduced to train network consistency, and we theoretically show that the proposed mixup naturally generates a tail-majority distribution mixup. For classifier training, we introduce balanced self-distillation guided knowledge transfer to improve generalization performance, where we theoretically show that proposed knowledge transfer implicitly minimizes not only cross-entropy but also KL divergence between head-to-tail and tail-to-head. Extensive experiments on long-tailed CIFAR10/100, ImageNet-LT and multilabel long-tailed VOC-LT demonstrate the proposed method's effectiveness.
Study on the Trajectory Positioning and Prediction Framework of Unmanned Aerial Vehicle based on Long Short-term Memory Neural Network

Unmanned aerial vehicles (U A V) have extensive application value in the military field. It has been deployed on a large scale in the conflict between Armenia and Azerbaijan, the civil war in Yemen and the war in Syria. Therefore, it is of great significance to deal with the threat of unmanned aerial vehicles. The current UAV traj ectory positioning and prediction methods have deficiencies such as low prediction accuracy, poor robustness, and small data scale. To improve the accuracy of positioning, reduce the positioning time, and improve the accuracy of trajectory prediction, this paper firstly uses the time difference information of the electromagnetic signal of the UA V received by the passive observation station, and then uses the Chan method to solve the flight traj ectory of the U A V according to the principle of time difference positioning. Further considering that the space trajectory of UAV is a complex time series with continuity and interaction, the trajectory of UAV can be predicted through long-term memory neural network. Hence, the study in this paper provides a new reference solution for U A V trajectory prediction analysis.
Research on Intelligent Detection Technology of Transparent Liquid based on Style Transfer

In order to solve the problem of transparent liquid perception, this paper proposes a transparent liquid detection method based on style transfer. The generative model based on contrastive learning is used to transform transparent liquid image into colored liquid image, which is equivalent to data enhancement of images. After that, the Yolov5 model is used to detect the colored liquid image. The experiment shows that the detection accuracy of this method is up to 99%, and it has good stability. It can accurately detect the transparent liquid in the container, and then help to estimate the area, liquid level height, volume and quality of the transparent liquid.
Product Success Evaluation Model Based on Star Ratings, Reviews and Product Popularity

In the era of the Internet and big data, it is of great significance to formulate product sales strategies and product improvement plans based on user evaluation data. Therefore, an evaluation system is established to calculate the index (product success index) composed of three factors: actual star rating of consumers, star rating of review analysis, and star rating of product popularity (star rating of products over time). We use AHP to determine the weights between these three factors. We then select the products we consider most successful based on the calculated product success index and found that users are most concerned about product quality, price and security. At the same time, to solve the question of whether a particular review affects subsequent ratings. We used a two-way ANOVA method to analyze whether specific reviews in the early stages would affect the stars in later stages. We found that the p-value is concentrated between 0.1-0.2, which means the specific star rating is considered to have an impact on the evaluation. Finally, we suggest that Merchants and platforms should pay more attention to the characteristics of successful products and early product feedback. The innovation of this paper is that it combines a variety of methods including systems science, management science, and statistical prediction artificial intelligence to comprehensively evaluate and predict the success of commodities, which can provide certain support for business decisions.
Continuous and Unified Person Re-Identification

Person re-identification (ReID) aims to match pedestrian images across disjoint cameras. Mainstream Re-ID tasks focus on training ReID models once using all the data, which become limited in some real-world scenarios where training data tends to arrive in stages. To match scenarios where training data is incrementally available, some works began to explore ReID task that can make efficient use of piecemeal new data. However, due to the limitations of the training and testing setups, these efforts are still preliminary explorations. In this paper, we explore a novel yet harder Continuous and Unified ReID (CUReID), which not only enables to continuously learn discrimination knowledge from data streams with style differences, but also to be uniformly evaluated discriminatory capability on all the data (seen and unseen). Furthermore, we propose a novel Generalized Feature Decoupled Learning (GFDL) framework for CUReID, which characterizes by introducing alternate training with extra images to solve the problem of optimization divergence between regularisation (learning new knowledge) and generalization (anti-forgetting old knowledge) tasks. In our newly proposed benchmark setup, GFDL achieves the state-of-the-art performance.
Vision-Based Hierarchical Impedance Control of an Aerial Manipulator

This article presents an image-based impedance control scheme for an under-actuated aerial manipulator. Unlike the existing Cartesian cascade control (admittance control) scheme, we design an image-space visual servoing controller at the dynamic level to achieve compliant interaction with unknown environments. Furthermore, compared with the state of the art in aerial interaction control, feedback of both the external wrench and the Cartesian position of the aerial manipulator are not required. To cope with the under-actuation property of the system, an attitude tracking control task is designed in such a way that it is dynamically decoupled with the visual impedance control task. Convergence of the task-space tracking error is proven based on the Lyapunov theory. The finite-gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal{L}}_2$</tex-math></inline-formula> stability of the system regarding external force/disturbance is also proven. Indoor experiments are conducted to demonstrate the proposed method.
I‐CenterNet: Road infrared target detection based on improved CenterNet

Infrared target detection has strong anti-interference ability, long working distance and can work day and night. So it is widely used in military security and transportation fields, and infrared road object detection is critical in traffic checkpoints and autonomous driving. However, the target scale in infrared images changes greatly, small targets are difficult to detect, the poor image quality and low signal-to-noise ratio are still huge challenges in infrared target detection. This paper proposes an improved infrared target detection model I-CenterNet based on the anchor-free model CenterNet. The EfficientNetV2 with the channel attention mechanism is used instead of the traditional structure as the backbone network to enhance feature extraction. In order to reduce the noise of the input infrared image, Dilated-Residual U-net (DRUNet) is used. Meanwhile, feature pyramid and Sub-Pixel are combined for multi-scale feature fusion. Data enhancement is implemented to improve model performance. The experimental results show that the average detection accuracy of this model on the Flir infrared data set is 87.9%, and the average detection speed reaches 14.2 frames/s.
Classification of Videos Based on Deep Learning

Automatic classification of videos is a basic task of content archiving and video scene understanding for broadcasters. And the time series modeling is the key to video classification. To solve this problem, this paper proposes a new video classification method based on temporal difference networks (TDN), which focuses on capturing multiscale time information for effective action classification. The core idea of TDN is to design an effective time module by clearly using the time difference operator and systematically evaluate its impact on short-term and long-term motion modeling. In order to fully capture the time information of the entire video, TDN has established a two-level difference model. For local motion modeling, the time difference in consecutive frames is used to provide a more refined motion mode for convolutional neural network (CNN). For global motion modeling, the time difference of the segments is combined to capture the remote structure for the extraction of the motion feature. The experimental results on two public video anomaly detection data sets, namely, UCF sports data set and SVW field sports data set, prove that the performance of the proposed method is better than some existing methods.
Psychological Analysis of Athletes during Basketball Games from the Perspective of Deep Learning

Due to the influence of psychological factors during sports, basketball players often change the shooting rhythm in sports, which will reduce the shooting rate and directly affect the training effect. Therefore, we need to analyze the basketball shooting process of some athletes, which is not a complete negation of traditional training methods, but a psychological analysis of some athletes’ shooting in the course of competition, so as to complement the feedback and training information after the game. With the great improvement of computer computing power, deep learning natural language processing can help people analyze and solve previously unsolvable problems in production and life. The psychological emotion of adolescence has a great influence on the study and life of middle school students. At present, the unified monitoring and analysis of the daily life of middle school students need not only a lot of manpower, but also slow speed. If the psychological problems are not found in time and feedback is given, it will cause a series of adverse effects on individual athletes. The neural network model based on deep learning can process students' daily mass text information quickly and accurately, and then give comprehensive judgment, which is a good solution. This paper applies the neural network algorithm of the Bi-LSTM model and CNN model to study the text data, and finally has 95.55% and 90.03% accuracy in the psychological analysis experiment, which provides a feasible solution to solve the batch rapid analysis of psychological changes reflected in the daily text of athletes during basketball. Some suggestions are put forward on how to strengthen and improve the psychological quality of college basketball players and their ability to bear pressure and difficulties.
A Plan for Applying Extended Reality to an English Textbook for Elementary School Students

The purpose of this study is to provide application plans for English textbooks for elementary school students through Extended Reality (XR). For this purpose, this study first investigated current trends and issues in the technology of XR including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). This study also investigated current studies of the application of XR technology in educational fields. Recent studies showed that applications of XR in English class for elementary school students were very rare. Only a few cases of applications of AR were found to be utilized for English class for elementary schools. Thus, this study suggested a direction of developing English education contents for elementary school students through the application of XR. A practical use of English textbook applied by XR is introduced on the basis of the contents of a textbook unit. Example sections of a class period in a textbook unit applied by XR were proposed. In each section, a developmental plan of class activities applied by XR was offered. In consideration of recent circumstances of technology, cost, and time, a developmental plan of class activities in each section applied was suggested in the order of VR, AR, and MR.
Symmetry‐Protected Two‐Level System in the H<sub>3</sub> Center Enabled by a Spin–Photon Interface: A Competitive Qubit Candidate for the NISQ Technology

Abstract Recent technological advances in the noisy intermediate‐scale quantum (NISQ) era are promising. Optically addressable color centers in semiconductors can achieve spin localization and are the most promising spin qubit candidates for NISQ technologies. Exploring a suitable and scalable atomic‐like color center is a prerequisite in this context. Here, a symmetry‐protected two‐level system (qubit) derived from a set of naturally separated Bell states in the H 3 center with C 2v symmetry is characterized. The characterized qubit significantly reduces magnetic noise compared with a degenerate triplet in C 3v systems for conventional qubits. The Hamiltonian, including the Coulomb interaction, spin–orbit coupling, and spin–spin interaction, is comprehensively developed using a combination of first‐principle calculations and group theory analyses. Consequently, an intrinsic spin–phonon interface embedded in an optical spin‐polarization loop can enable an effective interrogation of the information of the two‐level system. This study not only paves the way for developing further quantum information science applications utilizing the H 3 center but also provides a competitive qubit candidate for the NISQ era.
Describing Data in Image Format: Proposal of a Metadata Model and Controlled Vocabularies

Research data management (RDM) includes people with different needs, specific scientific contexts, and diverse requirements. The description is a big challenge in the domain of RDM. Metadata plays an essential role, allowing the inclusion of essential information for the interpretation of data, enhances the reuse of data and its preservation. The establishment of metadata models can facilitate the process of description and contribute to an improvement in the quality of metadata. When we talk about image data, the task is even more difficult, as there are no explicit recommendations to guide image management. In this work, we present a proposal for a metadata model for image description. To validate the model, we followed an experiment of data description, where eleven participants described images from their research projects, using a metadata model proposed. The experiment shows that participants do not have formal practices for describing their imagery data. Yet, they provided valuable contributions and recommendations to the final definition of a metadata model for image description, to date nonexistent. We also developed controlled vocabularies for some descriptors. These vocabularies aim to improve the image description process, facilitate metadata model interpretation, and reduce the time and effort devoted to data description.
Augmented Reality for Automatically Generating Robust Manufacturing and Maintenance Logs

Logs describing the execution of procedural steps during manufacturing and maintenance tasks are important for quality control and configuration management. Such logs are currently hand-written or typed during a procedure, which requires engineers to frequently step away from their work and results in difficulties for searching and optimizing logs. In this paper, we propose to automatically generate standardized, searchable logs, by visually perceiving and monitoring the progress of the procedure in real-time, and comparing this to the expected procedure. Unlike related work, we propose an approach which does not restrict the engineers to rigid, sequential sequences and instead allows them to execute procedures in a variety of different sequences where possible. The proposed framework is experimentally validated on the task of (dis)assembling a Duplo block model and operates properly when occlusions are absent.
Block-based progressive visual cryptography scheme with uniform progressive recovery and consistent background

Block-based progressive visual cryptography scheme (BPVCS) divides a secret image into non-overlapping blocks and encodes each block as sub-shadows. The final shadows for BPVCS are created by combining the associated sub-shadows. When enough shadows are superimposed, some of the secret blocks will be exposed. More information will be revealed as more shadows are used. This is referred to as progressive recovery. Hou et al. introduced a ( 2 , n ) -BPVCS. Yang et al. further extended the ( 2 , n ) scheme to a general ( k , n ) scheme. However, Yang et al. ( k , n ) -BPVCS suffers from the non-uniform progressive recovery and inconsistent background of recovered secret blocks. In this paper, we introduce a ( k , n ) -BPVCS to address the mentioned two defects. Theoretical analysis and experimental results are provided to illustrate the benefits of the proposed approach.
Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor. Deep learning models can learn subtle cues in the mammogram images. CNN models were recently shown to improve the risk discrimination in full-field mammograms. This study aims to improve risk prediction models using bilateral analysis. Bilateral analysis is the process of comparing two breasts to verify presence of anomalies. We developed a Siamese neural network to leverage the bilateral information and asymmetries between the two mammograms of the same patient. We tested our model on 271 patients and compared the results of our Siamese model against the traditional unilateral CNN model. Our results showed AUCs of 0.75 and 0.70 respectively (p = 0.0056). The Siamese model also exhibits higher sensitivity, specificity, precision, and false positive rate with values of 0.68, 0.69, 0.71, 0.31 respectively. While the CNN values were 0.61, 0.66, 0.67, 0.34 respectively. We merged both models by two techniques using pre-trained weights and weighted voting ensemble. The merging technique boosted the AUC to 0.78. The results suggest that bilateral analysis can significantly improve the risk discrimination.
Autonomous Rear Parking via Rapidly Exploring Random-Tree-Based Reinforcement Learning

This study addresses the problem of autonomous rear parking (ARP) for car-like nonholonomic vehicles. ARP includes path planning to generate an efficient collision-free path from the start point to the target parking slot and path following to produce control inputs to stably follow the generated path. This paper proposes an efficient ARP method that consists of the following five components: (1) OpenAI Gym environment for training the reinforcement learning agent, (2) path planning based on rapidly exploring random trees, (3) path following based on model predictive control, (4) reinforcement learning based on the Markov decision process, and (5) travel length estimation between the start and the goal points. The evaluation results in OpenAI Gym show that the proposed ARP method can successfully be used by minimizing the difference between the reference points and trajectories produced by the proposed method.
Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis

Data from the Ministry of Civil Works and Public Housing (Kementrian PUPR) in 2019 shows that around 81 million millennials do not own houses. Government Regulation Number 25 of 2020 on the Implementation of Public Housing Savings, commonly called PP 25 Tapera 2020, is one of the government's efforts to ensure that Indonesian people can afford houses. Tapera is a deposit of workers for house financing, which is refundable after the term expires. Immediately after enaction, there were many public responses regarding the ordinance. We investigate public sentiments commenting on the regulation and use Support Vector Machine (SVM) in the study since it has a good level of accuracy. It also requires labels and training data. To speed up labeling, we use the lexicon-based method. The issue in the lexicon-based lies in the dictionary component as the most significant factor. Therefore, it is possible to update the dictionary automatically by combining lexicon-based and SVM. The SVM approach can contribute to lexicon-based, and lexicon-based can help label datasets on SVM to produce good accuracy. The research begins with collecting data from Twitter, preprocessing raw and unstructured data into ready-to-use data, labeling the data with lexicon-based, weighting with TF-IDF, processing using SVM, and evaluating algorithm performance model with a confusion matrix. The results showed that the combination of lexicon-based and SVM worked well. Lexicon-based managed to label 519 tweet data. SVM managed to get an accuracy value of 81.73% with the RBF kernel function. Another test with a Sigmoid kernel attains the highest precision at 78.68%. The RBF kernel has the highest recall result with a value of 81.73%. Then, the F1-score for both the RBF kernel and Sigmoid is 79.60%.