Reviewerly/exHarmony huggingface dataset:
Partial Differential Equation Noise Reduction Model and Fuzzy Image Processing in Optimal Application of Sports Dance Exercise Training Mode

Ballroom dancing has been standardized in the United Kingdom since the beginning of the 20th century, and it has been competitive by WDC to form competitive ballroom dancing. Later, under the vigorous promotion of WDSF, it became a competitive sport recognized by the International Olympic Committee. In the process of teaching experiments, dancers’ wrong movements in sports dance training are inevitable. If they are not corrected in time, it will not only reduce the effect of dance expression, but also directly affect the improvement of sports performance. The dance images are often disturbed by many factors such as imaging equipment and the external environment during the process of acquisition, compression, transmission and storage, resulting in image distortion. Image distortion is mainly manifested in that the image structure information is submerged by noise, and the distorted image can no longer truly reflect the original image information, which brings difficulties to the post-processing of the image. In this paper, the image denoising method based on partial differential equation and its application in sports dance training are deeply studied.
Application of Image Recognition Based on Wireless Sensors in Dance Teaching System

With the vigorous development of higher education in China, many universities have made great progress in various indicators in recent years. As the number of college students increases year by year, the effect of instruction in the classroom is especially important. The high quality of teaching directly affects the efficiency of students’ listening to lectures, and more and more universities are receiving attention. However, the traditional dance classroom education and the one-to-many education model cannot adapt to the development trend of higher art education under the changes of the times and cannot effectively guarantee the quality of classroom education. The development of wireless sensor networks provides practical and feasible technical solutions for the development of dance education systems. Compared with general detection methods, image sensors can provide more real-time and more intuitive on-site information and wirelessly send image information to user terminals. This article describes the classic feature extraction algorithm and proposes a new feature extraction algorithm based on chart filling. The effectiveness of each algorithm is verified through several data sets. Image recognition is carried out by computer, including from computer to image processing, through the computer to recognize objects and various different modes of the target technology. The identification process usually includes several steps. First, the preprocessing of the image is required, then the segmentation of the image is performed, and then the feature extraction and matching are performed. In layman’s terms, image recognition hopes to imitate the human heart to read photos. By applying the image recognition technology to the dance education system, changes in the methods and forms of dance education can be stimulated.
Employment of Machine Learning and Data Mining Technique in Hybrid Recommender System: A Comprehensive Survey

In the present time of advanced world, personalization of data is generally significant for addressing user's needs more adequately, in this way expanding consumer loyalty. Recommender system has become a compelling data filtering tool that reduces data overburden for web users. Hybrid recommender systems consolidate at least two recommendation algorithms so as to increase better system performance to maintain strategic limitations from certain restrictions and issues of pure recommender systems. The article investigates the cutting edge in hybrid recommender system from the most recent decade. The paper tended to the most significant issues considered and recommendation technique incorporated to overcome. This paper examines hybridization classifications, the application areas, and the evaluation procedure and proposed future research direction. The greater part of the investigations joins collaborative filtering with another machine learning strategy. Hybrid recommender system is a hot subject in the digital world and represents newer opportunities to the researcher such as contextualizing recommendation algorithm, involving parallel hybrid algorithm, cross recommendation, and scalability.
Interaction Design of Educational App Based on Collaborative Filtering Recommendation

With the advent of the 5G digital era, cell phones are becoming ubiquitous in all aspects of our lives, and the increasing demand for remote interaction makes the app interaction experience an indispensable part of our lives. Due to the operational characteristics of gesture interaction in the interface of a smart terminal application (app), this mode of human-computer interaction has become the mainstream mode of human-computer interaction. Educational app is the result of a combination between mobile Internet technology and education, which not only provides a more efficient and convenient method of learning for each subject but also expands the possibilities for teaching each subject through intelligent interaction. On this basis, this paper proposes an educational app design method based on collaborative filtering recommendations and investigates ways to improve the use of mobile apps to create an interactive teaching mode. Simultaneously, this paper combines user activity, item popularity, and time factors to comprehensively measure user visibility of items and incorporates them into the collaborative filtering recommendation algorithm in order to effectively mitigate the effects of data sparsity and user selection bias and improve recommendation results.
Optimal parameter estimation without consuming raw keys for continuous-variable quantum key distribution

Abstract In conventional continuous-variable quantum key distribution using single Gaussian modulation (SGM), it is usually necessary to reveal a portion of the raw keys to estimate the parameters of the quantum channel. In the double Gaussian modulation (DGM) protocol, parameter estimation is performed without consuming raw keys at the cost of having the estimated result be affected by more aggregated noise than the SGM protocol. In this paper, inspired by the idea of performing parameter estimation after error correction, we propose two improved DGM protocols, i.e., the Gaussian dense modulation protocol, and the Gaussian separate modulation protocol, to eliminate this cost. The two DGM protocols perform parameter estimation twice, once before and once after error correction, so that all raw keys can be used for both parameter estimation and key extraction. Therefore, when finite-size effects are considered, the optimal parameter estimation of the protocol is achieved.
Exploiting more associations between slots for multi-domain dialog state tracking

Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the correlation between slots, so that the slots can incorporate more slot-value transformer information. Experimental results show that our approach has performed well on the Multi-domain Wizard-Of-Oz (MultiWOZ) 2.0 and MultiWOZ2.1 datasets, demonstrating the effectiveness and necessity of incorporating slot-relevant information.
Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units

Extubation is the process of removing tracheal tubes so that patients maintain oxygenation while they start to breathe spontaneously. However, hypoxemia after extubation is an important issue for critical care doctors and is associated with patients' oxygenation, circulation, recovery, and incidence of postoperative complications. Accuracy and specificity of most related conventional models remain unsatisfactory. We conducted a predictive analysis based on a supervised machine-learning algorithm for the precise prediction of hypoxemia after extubation in intensive care units (ICUs).Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database for patients over age 18 who underwent mechanical ventilation in the ICU. The primary outcome was hypoxemia after extubation, and it was defined as a partial pressure of oxygen <60 mmHg after extubation. Variables and individuals with missing values greater than 20% were excluded, and the remaining missing values were filled in using multiple imputation. The dataset was split into a training set (80%) and final test set (20%). All related clinical and laboratory variables were extracted, and logistics stepwise regression was performed to screen out the key features. Six different advanced machine-learning models, including logistics regression (LOG), random forest (RF), K-nearest neighbors (KNN), support-vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were introduced for modelling. The best performance model in the first cross-validated dataset was further fine-tuned, and the final performance was assessed using the final test set.A total of 14,777 patients were included in the study, and 1,864 of the patients' experienced hypoxemia after extubation. After training, the RF and LightGBM models were the strongest initial performers, and the area under the curve (AUC) using RF was 0.780 [95% confidence interval (CI), 0.755-0.805] and using LightGBM was 0.779 (95% CI, 0.752-0.806). The final AUC using RF was 0.792 (95% CI, 0.771-0.814) and using LightGBM was 0.792 (95% CI, 0.770-0.815).Our machine learning models have considerable potential for predicting hypoxemia after extubation, which help to reduce ICU morbidity and mortality.
Attention of Many Observers Visualized by Eye Movements

Interacting with a group of people requires to direct the attention of the whole group, thus requires feedback about the crowd’s attention. In face-to-face interactions, head and eye movements serve as indicator for crowd attention. However, when interacting online, such indicators are not available. To substitute this information, gaze visualizations were adapted for a crowd scenario. We developed, implemented, and evaluated four types of visualizations of crowd attention in an online study with 72 participants using lecture videos enriched with audience’s gazes. All participants reported increased connectedness to the audience, especially for visualizations depicting the whole distribution of gaze including spatial information. Visualizations avoiding spatial overlay by depicting only the variability were regarded as less helpful, for real-time as well as for retrospective analyses of lectures. Improving our visualizations of crowd attention has the potential for a broad variety of applications, in all kinds of social interaction and communication in groups.
Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles

To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate nonessential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively.
3D Visualization Analysis of Motion Trajectory of Knee Joint in Sports Training Based on Digital Twin

With the development of China’s social economy, the pace of people’s life is also accelerating. Therefore, in the face of the increasing pressure of life and work, people have begun to pay more and more attention to improving their physical fitness and they also pay more and more attention to sports in their spare time. The knee joint is a very important body part that will be involved in sports. Therefore, in sports, it is very important for healthy sports to pay attention to the state of the knee joint at all times and avoid injury to the knee joint during sports. The three-dimensional visual analysis of the knee joint movement trajectory in sports training refers to the trajectory capture and three-dimensional visual analysis of the knee joint movement in sports training based on the computer-based description and understanding of model technology. The three-dimensional visual analysis of the motion trajectory of the knee joint in sports training is conducive to the attention and analysis of the motion state of the knee joint in sports training, so as to help find problems and avoid knee joint damage during sports training as much as possible. This paper aims to study the 3D visualization analysis of knee joint motion trajectory in sports training based on digital twin and establish a 3D visualization system of knee joint motion trajectory based on digital twin technology, and based on this system, the three-dimensional visualization experiment of knee joint movement trajectory in sports training is carried out. The experiment concluded that the signal-to-noise ratio of the three-dimensional visualization image of the knee joint movement trajectory in sports training based on the three-dimensional visualization system established by the digital twin is 68% and the clarity is 89%. It shows that the system has a good 3D visualization effect.
Trade or Trick?

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
Time-dependent quantum teleportation via a parametric converter

Abstract Quantum state teleportation is an important protocol that plays a pivotal role in various quantum information tasks. Here we theoretically investigate quantum state teleportation by exploiting a general two-mode Gaussian entangled state produced by a parametric converter when two single-mode Gaussian states (SMGSs) in terms of the non-classicality and purity are employed as inputs to the parametric converter. In particular, the time-dependent teleportation fidelity is analyzed with respect to the squeezing parameter and phase-space quadratures of the teleported squeezed coherent state. We show that the teleportation fidelity is maximal when ratio of the expectation values of photon number in the two modes of the evolved Gaussian entangled state is equal to 1. Quantum state teleportation in terms of the purity and non-classicality of the general two SMGSs seems to be a good choice for experimental realization of quantum communication and information processing.
Sex estimation from biometric face photos for forensic purposes

Sex estimation is an important parameter in cases where individuals need to be identified in forensic cases. Biometric photographs are a form of a passport photo with specific dimensions and features established by the International Civil Aviation Organization, which are read and digitally stored in appropriate devices, are used in travel documents, and are of high quality (at least 600 dpi). This study aims to reveal anthropometric data for estimating sex in Turkish adult population from facial images conforming to biometric photography criteria. Within the scope of this study, biometric facial images of a total of 334 participants, 146 female and 188 male, between the ages of 20 and 79 were used. The photos were taken using a Nikon D5100 and flat front lighting from a distance of 1 m. ImageJ 1.50i software was used to process these images. Statistical analysis was performed using descriptive statistics and discriminant analysis tests. Among the 11 variables on sex estimation, the highest accuracy rate of 78.1% was obtained with the measurement between Gonion points. However, sex estimation could be made with an accuracy of 80.5% by including all age groups and all variables, and when age-specific data were used, it was observed that these accuracy rates increased significantly in all three age groups (84.6%, 89.2%, 85.2%, respectively). Therefore, we are suggesting that using age-specific data generated for estimation in different age groups. Consequently, it has been shown that successful sex estimation can be done by formulas derived from biometric facial images in this study.
Convolutional Neural Network Based Real Time Arabic Speech Recognition to Arabic Braille for Hearing and Visually Impaired

Natural Language Processing (NLP) is a group of theoretically inspired computer structures for analyzing and modeling clearly going on texts at one or extra degrees of linguistic evaluation to acquire human-like language processing for quite a few activities and applications. Hearing and visually impaired people are unable to see entirely or have very low vision, as well as being unable to hear completely or having a hard time hearing. It is difficult to get information since both hearing and vision, which are crucial organs for receiving information, are harmed. Hearing and visually impaired people are considered to have a substantial information deficit, as opposed to people who just have one handicap, such as blindness or deafness. Visually and hearing-impaired people who are unable to communicate with the outside world may experience emotional loneliness, which can lead to stress and, in extreme cases, serious mental illness. As a result, overcoming information handicap is a critical issue for visually and hearing-impaired people who want to live active, independent lives in society. The major objective of this study is to recognize Arabic speech in real time and convert it to Arabic text using Convolutional Neural Network-based algorithms before saving it to an SD card. The Arabic text is then translated into Arabic Braille characters, which are then used to control the Braille pattern via a Braille display with a solenoid drive. The Braille lettering triggered on the finger was deciphered by visually and hearing challenged participants who were proficient in Braille reading. The CNN, in combination with the ReLU model learning parameters, is fine-tuned for optimization, resulting in a model training accuracy of 90%. The tuned parameters model's testing results show that adding the ReLU activation function to the CNN model improves recognition accuracy by 84 % when speaking Arabic digits.
Target-aware Abstractive Related Work Generation with Contrastive Learning

The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Authors can save their time and effort by using the automatically generated related work section as a draft to complete the final related work. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences to make a comparative discussion about the target work and the reference papers. However, such sentences need to be written in advance and are hard to obtain in practice. Hence, in this paper, we propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences. Concretely, we first propose a target-aware graph encoder, which models the relationships between reference papers and the target paper with target-centered attention mechanisms. In the decoding process, we propose a hierarchical decoder that attends to the nodes of different levels in the graph with keyphrases as semantic indicators. Finally, to generate a more informative related work, we propose multi-level contrastive optimization objectives, which aim to maximize the mutual information between the generated related work with the references and minimize that with non-references. Extensive experiments on two public scholar datasets show that the proposed model brings substantial improvements over several strong baselines in terms of automatic and tailored human evaluations.
Hardware in The Loop Simulation for robot Navigation with RFID

The main goal of this article is to propose a software architecture allowing the integration of RFID (Radio Frequency Identification ) sensors in the motion strategy for a simulated mobile robot. RFID systems are composed of antennas, readers and Tags. Passive RFID Tag consists of a memory storing information. The reader reads/writes information from/on the Tags through the antennas. In robotic context, the Tags contain information about the environment surrounding the robot such as obstacles, humans, other robots, and different types of objects etc. The RFID system reads Tags data, and controls the movement of the simulated mobile robot consequently (stops, changes its velocity, regulating its speed, setting a new destination …) based on the information stored in the Tags. Hardware In the Loop Simulation (HILS) system is proposed. The RFID device is the hardware system and the virtual mobile robot is the Simulated system.