Reviewerly/exHarmony huggingface dataset:
Face recognition based on deep feature mapping to kernel space

Deep face recognition has attracted more and more researchers' attention due to its layer-by-layer feature learning. However, the classical sparse representation methods have strong classification ability. Considing above two advantages of deep learning and sparse representation, this paper proposed a face recognition algorithm based on deep local dictionary (DLD) feature optimization combining weighted and joint kernel collaborative representation (WJKCR) classification. The main contribution of this paper as follows: (1) the normal probability density function is used to extract local blocks from multi-angle face images. (2) Deep CNN and transfer learning theory were used to extract features from image blocks, and the feature set was selected as the optimal local feature dictionary. (3) Use the weighted and joint kernel collaborative representation for face classification to verify the effectiveness of our algorithm.
Chinese medical named entity recognition based on ensemble column-wise convolution

For the named entity recongnition task, with an uneven distribution of entities, the model usually fails to correctly identify minor entities because of the influence of other major entities Therefore, a name entity recognition model based on ensemble column-wise convolution is proposed. Column-wise convolution performs the corresponding convolution operation on each dimension of the word embedding separately, and then concatenates the results into a final feature. Combined with the integrated network architecture, it enables access to richer semantic information to further enhance the generalisation of the model. Experiments were conducted on CMeEE datasets and the model reached 69.47% levels in F1-score, confirming that they were both better than the other comparison models., and there is a significant improvement in the accuracy of recognition of minor entities.
A Method of Mask-Wearing Detection Based on Improved YOLOv4

In this paper, a lightweight network based on improved YOLOv4 is proposed to solve the problems of complex model structure and unsatisfactory performance of detection speed in fast-moving situations. Firstly, the inverted residual blocks (IRB) based on Mobilenetv2 are adopted into the backbone feature extraction network to reduce the complexity of the model structure. Then, the feature fusion network based on the depth-wise separable convolution is applied to minimize model calculations and parameters. Experimental results show that the proposed model has the advantages of high accuracy, fast detection speed, and lightweight, which has satisfied the requirements of real-time detection of mask-wearing in fast-moving situations.
The Iris Cassification Based on Gaussian Naive Bayes Agorithm

Bayes' theorem is one of the most famous theories in probabilistic models, which is adept to be combined in machine learning, especially in classification applications. IRIS set is the classical dataset of machine learning. Firstly, naive and Gaussian Bayes classifier are provided including their relationship. Secondly, preprocess IRIS dataset, use CSV Format or read dataset into a Pandans DataFrame, perform prior, divide them into training and test datasets in proportion. And then, perform train and test work according to corresponding data with Gaussian Bayes classifier. Finally, carry out cross validation and analyze the accuracy of the verification results. The emphasis is on the working of naive Bayes model with Gause principle and the improvement on the method of dataset feature extraction.
Handwritten number recognition system based on Image processing

Numbers recognition requires a person to present a handwritten number, which can be recognized from the designed system. This paper demonstrates the handwritten number recognition system through the image preprocessing algorithms and image binarization method in detail. Also, this paper introduces the meaning and generalization of the handwritten number recognition system. The results from the analysis show that the proposed image processing method for handwritten number recognition is effective.
Sonar Pulse Detection and Recognition Based on Deep Denoising

In view of the slow convergence of the typical denoising network, which is prone to excessive smoothing of images and insufficient recovery of detailed features, this paper proposed a deep denoising network architecture based on the auto-encoder network and the generative adversarial network. That took advantage of the adversarial learning of generators and discriminators, making denoising results sharper and clearer, improving the over-smoothing issue of images, and enhancing the restoration of specific information. Meanwhile, the residual structure was introduced into encoders of the auto-encoder network, which further augmented their denoising capacity and accelerated the convergence of the loss function of the generative adversarial network. In the end, the YOLOv3 network was respectively employed to detect the sonar pulse power spectrum images before and after denoising and analyze the performance of the proposed deep denoising network in the sonar pulse detection. The experimental results manifest that both the power signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the denoised image are improved, compared with those of the low signal-noise sonar pulse power spectrum image and that the target detection accuracy is remarkably lifted, which proves the proposed the effectiveness of the deep denoising network in improving the quality of the power spectrum image about the sonar pulse signal, and the significance of denoising process in the sonar pulse detection.
Query Attack by Multi-Identity Surrogates

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable Adversarial Examples (AEs) because of their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gradient Similarity (GS)</i> , i.e., surrogates' attack gradients are similar to the victim's ones. However, it is generally neglected to exploit their similarity on outputs, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prediction Similarity (PS)</i> , to filter out inefficient queries by surrogates without querying the victim. To jointly utilize and also optimize surrogates' GS and PS, we develop QueryNet, a unified attack framework that can significantly reduce queries. QueryNet creatively attacks by multi-identity surrogates, i.e., crafts several AEs for one sample by different surrogates, and also uses surrogates to decide on the most promising AE for the query. After that, the victim's query feedback is accumulated to optimize not only surrogates' parameters but also their architectures, enhancing both the GS and the PS. Although QueryNet has no access to pre-trained surrogates' prior, it reduces queries by averagely about an order of magnitude compared to alternatives within an acceptable time, according to our comprehensive experiments: 11 victims (including two commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image queries, and no access to the victim's training data. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sizhe-Chen/QueryNet</uri> .
Data-and-Knowledge Dual-Driven Radio Frequency Fingerprint Identification

Wireless network security can be improved by radio frequency fingerprinting due to its stability and uniqueness. Although many radio frequency fingerprint identification (RFFI) methods based on deep learning have been proposed, they have low identification accuracy, especially at low signal-to-noise ratio. In order to overcome this drawback, a data-and-knowledge dual-driven RFFI scheme is proposed by utilizing a knowledge-driven multiscale attention convolutional network (AttMsCN). The protocol knowledge is exploited to provide more advanced semantics. Moreover, the AttMsCN is utilized to capture higher level features. Simulation results demonstrate that our designed scheme has the best performance than the representative schemes in the matter of convergence speed and identification accuracy.
Native Languages of the Democratic Republic of Congo

DRC is a multilingual country where more than 200 native languages are spoken among a population of 92,798,854 inhabitants. Language variety strongly evidences the genetic force of the human language faculty for creating symbolic systems. Congolese native languages spoken by local communities are unique and precious coding systems that constitute an important asset of the heritage of the DRC. Today‮’‬‎s science and technology focuses on developing cutting-edge Artificial Intelligence technology. In that aspect, the rich language variety of DRC is of great advantage, since much of AI Technology is applied to natural language data. Moreover, many Congolese people have become by now highly educated scientists who could lead digital innovative groups which would take care of preserving their valuable native languages. So it is a great historical moment for transforming Congolese linguistic data into industries that would return progress and social welfare to the people. I review some outstanding linguistic properties of 3 bantu languages (Cilubà, Lɔmɔ́ngɔ, Nzadi), and asses the depth of knowledge and expertise of their mother tongues by the Congolese population and the use of these indigenous languages in education. I then consider the adequacy of Education Plans to boosting, promoting and preserving the unique multilingual wealth of DRC.
Study on the online learning evaluation based on 2D and 3D image processing technology

In recent years, the outbreak of the COVID-19 epidemic has posed a serious threat to the life safety of people around the world, which has also led to the development of a series of online learning assessment technologies. Through the research and development of a variety of online learning platforms such as WeChat, Tencent Classroom and Netease Cloud Classroom, schools can carry out online learning assessment, which also promotes the rapid development of online learning technology. Through 2D and 3D recognition technology, the online learning platform can recognize face and pose changes. Based on 2D and 3D image processing technology, we can evaluate students' online learning, which will identify students' learning state and emotion. Through the granulation of teaching evaluation, online learning platform can accurately evaluate and analyze the teaching process, which can realize real-time teaching evaluation of students' learning status, including no one, many people, distraction and fatigue. Through relevant algorithms, the online learning platform can realize the assessment of students' head posture, which will give real-time warning of learning fatigue. Firstly, this paper analyzes the framework of online learning quality assessment. Then, this paper analyzes the face recognition and head pose recognition technology. Finally, some suggestions are put forward.
Research on key points recognition and interaction of 3D VR human body

With the rapid development of science, VR technology has become more mature and perfect, and has been widely used in various fields. Include human motion scanning, so as to improve the efficiency of human key point identification and interaction. Based on this, this paper designs an interactive and key point recognition scheme of human dynamic gestures by means of theoretical analysis and experimental analysis. Through experimental verification, it can be found that the gesture recognition rate of this scheme is high, with an average of 93.5% and high accuracy, and it can be popularized in practice.
Hazardous action recognition system based on blazepose and ST-recurrent neural network

This paper focuses on the recognition and classification of driver's dangerous driving actions through Blazepose algorithm and st-gru network to ensure that drivers can drive safely during the driving process and keep drivers safe at all times. blazepose is a lightweight human posture estimation model using blazepsoe method to replace the openpose method in human skeletal keypoints to improve the speed and reduce the model size. The st-gru network is one of the best action recognition models based on human skeletal keypoints, which is better than most of the current action recognition models in terms of model size, accuracy and recall value. Therefore, this project uses the st-gru network to classify the extracted human skeletal keypoint.
Dance somersault gesture recognition based on multi-scale depth feature fusion

In order to explore the problem of dance somersault gesture recognition, a kind of dance somersault gesture recognition based on multi-scale depth feature fusion is proposed. Methods Through the information recommendation of key technical problems and solutions based on multi-scale depth feature fusion, the research of dance somersault gesture recognition was explored. The research shows that the efficiency of dance somersault gesture recognition based on multi-scale depth feature fusion is about 4.6% higher than that of traditional methods. The acquisition of main video information has always been inclined to obtain video key frames. However, in the face of videos with strong continuity and low repetition between human posture sequences in various movements, only key frames can't represent all the effective information of the videos. Most algorithms excessively pursue the differences between action categories, thus ignoring the degree of "cohesion" between simple actions within actions.
Research on application mode of intelligent accounting information based on cloud computing

With the in-depth development of science and technology, computer based multi disciplines have become the focus of research. Cloud computing is a virtual data information mode, which not only reduces the computational complexity of the server, but also saves a lot of time. Therefore, cloud computing has become the most important research topic. In the modern management of enterprises, accounting informatization is an indispensable part of improving the operating efficiency of enterprises, which will constantly improve financial management, efficiency and supervision. Cloud computing is a mode of virtualizing data through software services, which is a related method of service related data computing without related hardware facilities. Through cloud computing, enterprises can store business information in remote service terminals, which will continuously improve the efficiency of enterprise processing. At present, cloud computing has become a new way of accounting informatization, which has replaced the traditional way of accounting informatization. As data virtualization becomes the norm, cloud computing has become the main line of system services, which will be more conducive to resource sharing of accounting informatization. First, this paper analyzes the related concepts of cloud computing. Then, this paper analyzes the influencing factors of accounting informatization, including internal and external environmental factors. Then, this paper analyzes the advantages of accounting informatization. Finally, some optimization measures are proposed
Research on crack identification method of concrete structure based on digital image information analysis

Most of the subway stations are below the groundwater level, so it is particularly important to do a good job in waterproofing. For the leakage disease of subway station structure, detection is the method and identification is the purpose. There are many types of urban subway station structural leakage diseases, and it is not easy to identify the diseases in all directions. Based on the summary and analysis of the common identification methods of seepage water diseases in subway stations, the crack digital images obtained in a non-contact way are taken as the research objects. Starting from the crack characteristics and the principle of digital image processing algorithm, the traditional algorithm is improved and optimized to obtain a detection algorithm more suitable for the digital image of concrete structural cracks, which is applied to the identification of structural cracks in subway stations. Compared with other manual methods, this method is more accurate and can save a lot of time and cost.
Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

At present, the parallel computing theory based on spatial big data has problems such as difficult algorithms, difficult operations, and complex formulas, based on this, this paper proposes a p-Dot parallel computing model based on the traditional parallel computing model of BSP (Bulk Synchronous Parallel), and then tests the model effect by setting experiments. The results reveal that: (1) All curves are open up and have a minimum value. (2) The dataset with a capacity of 0.25GB is the benchmark dataset. (3) The expansion rate e(w) of the input data capacity of the model under different test procedures has a linear relationship with the expansion rate e(n* ) of the corresponding optimal number of machines. (4) When 𝑛→∞ in the partition equation p(n), p(n) tends to a certain value.
Data center information Atte encrypributtion method based on hash algorithm

As the key basic supporting platform of electric power enterprises, the data center often has internal attribute revocation, which seriously affects the efficiency of information attribute encryption. This paper proposes a method of information attribute encryption for data centers based on a hash algorithm. Then, update that data platform information, ensuring that the encryption method has low overhead and high efficiency. Extract the attribute of the data platform information based on a hash algorithm and encrypt the attribute of the information. The simulation results show that the proposed method occupies only 24% of the task process. The encryption time is relatively short, which verifies that the method has low overhead and high efficiency in the process of information attribute encryption and has a certain contribution value to ensure the information security of the data center.
Face aging on SiGan

Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.
Eye tracking for high fidelity augmented reality: requirements and challenges (Conference Presentation)

Augmented and Virtual Reality (AR/VR) displays differ from regular displays because they hack a human perception system, by creating an illusion of 3D objects that appear to be a part of the real world. Imprecision in implementation of AR system break this illusion and cause us a discomfort or even make us sick. On the other hand, since users are wearing the system on their heads, they are free to move around, their hands are also free, and they want them to stay free to perform the usual tasks. This creates huge and not fully resolved user interface issues - there are no "mouse and keyboard" solution for AR systems. Eye tracking plays a huge role and is one of the cornerstones of what I call high-fidelity Augmented Reality systems that feel comfortable, are easy to use, and do not cause sickness. We will discuss how eye tracking system may help to make better AR, challenges of current eye tracking technologies and what properties of eye tracking systems are important for Hi-Fi AR.
Metrology and alignment solutions applied for pancake lenses used in VR systems (Conference Presentation)

A current challenge for VR headsets producers is to reduce the weight and size of headsets. One very promising approach is the use of pancake lenses that help reducing the weight of the VR lens system but at the same time significantly increase the optical quality. Due to their complexity pancake lenses confront manufacturers with many challenges in testing and fabrication. TRIOPTICS´ expertise in active alignment and optical metrology enable our customers to create better pancake lenses for the challenges of tomorrow.
Track planning and design of autonomous obstacle avoidance for unmanned ships in complex environments

Route planning is an essential and important part of unmanned aerial vehicle (UAV) operations at sea. Therefore, this paper designs the trajectory planning for autonomous obstacle avoidance of unmanned ships in complex environments. Adopt the body coordinate system and inertial coordinate system to confirm the coordinates and heading angle of the unmanned ship; improve the inertia weight, determine the space constraints of the track planning, and accurately determine the autonomous obstacle avoidance path of the unmanned ship. Simulation experiments show that the trajectory planning method for autonomous obstacle avoidance of unmanned ships in complex environments designed in this paper reduces the time consumption of navigation, has stronger real-time performance, and can approximately represent the global optimal trajectory.
Pre-service English Teachers’ Perception and Practices of Digital Literacy: The Case of Hong Kong

Computer-assisted language learning (CALL) plays a crucial role in second/foreign language education. Enormous changes in technology and the growing emphasis on 21st century skills, especially digital literacy, raise a concern about English as a second/foreign language (ESL/EFL) learners’ and teachers’ competence in CALL more than ever. In response to the mounting concern, this study investigates 92 pre-service English teachers’ digital literacy in Hong Kong. This study reports on the participants’ digital literacy level as well as factors affecting their use of digital technologies for teaching English. Results of the study uncover pre-service English teachers’ current practice and knowledge of digital literacy and attitude toward digital technology in language teaching and learning. Finally, the paper identifies limitations of the study and provides suggestions to improve digital literacy surveys as well as possible future research.