Reviewerly/exHarmony huggingface dataset:
Research on error correction method of Tibetan text based on deep learning

In order to avoid too many Tibetan text errors from affecting the text quality, a method of Tibetan text error correction based on soft masked Bert is proposed. In this work, the definition and classification of Tibetan text error correction are pointed out, the embedding composed of word embedding, position embedding and paragraph embedding is used as the input of Tibetan text error detection model. The probability labels of characters in the text correspond to the output of the error detection model. The output of the model detects characters with high error probability.The final soft masked embedding result is obtained by the output of the weighted sum error detection model and the input embedding. The best model constructed by the encoder layer sets all soft masked embedding sequences as input, and the residual connection values are obtained by connecting the input embedding results and all hidden states of the encoder layer. The obtained residual connection values are input to the full connection layer, and the softmax function is used to obtain the probability that the output characters can be accurately corrected as candidate characters to complete Tibetan text error correction. Experimental results show that this method can achieve efficient Tibetan text error correction, and use the error probability of different candidate words to obtain the best error correction results.
Quantum-Communication using Multicore Fibers

Quantum communication represents a key enabler for many applications from secure communications to advanced quantum simulations on the cloud. We here report our recent results on the generation, transmission and detection of high-dimensional quantum states exploiting multicore fibers.
Research on Intelligent Security System of Online Car Based on Computer Vision

The online car is the product of the “Interonline+ ” era. As a new city intelligent transportation mode, it breaks the old interest pattern and the barrier of taxi industry based on franchise. In recent years, the number of online car Hailing has increased sharply. However, due to the lack of effective supervision means, security problems have gradually emerged, such as rape, robbery, intentional injury, intimidation, abuse of passengers and other incidents. In order to solve this problem, this project proposes an intelligent security system for online car Hailing based on machine vision, constructs an advanced machine vision solution framework, and introduces graphics and image processing technology and machine learning abnormal behavior intelligent warning model. At the same time, combined with the embedded technology, it can automatically warn the abnormal behavior in the online car Hailing and quickly access the manual intervention and alarm, so as to timely detect the security threats and potential dangers in the process of taking the bus, so as to realize the purpose of protecting the safety of the online car Hailing passengers.
Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources

This paper presents a technique that employs linguistic resources to refine PoS tagging using the Universal Dependencies (UD) model. The technique is based on the development and use of lists of non-ambiguous single tokens and non-ambiguous co-occuring tokens in Portuguese (regardless of whether they constitute multiword expressions or not). These lists are meant to automatically correct the tags for such tokens after tagging. The technique is applied over the output of two well-known state of the art systems - UDPipe and UDify - and the results for a real data set have shown a significant improvement of annotation accuracy. Overall, we improve tagging accuracy by up to 1.4%, and, in terms of the number of fully correct tagged sentences, our technique produces results that are 13.9% more accurate than the corresponding original system.
Automated Lymph Nodes Classification using Factorized Convolution-based CNN

Lymph nodes (LNs) are an essential component of immune system which filter out harmful substances. Any disease of LNs leads to serious medical complications and may reduce fighting spirit of human body. Quantitative assessment of LNs like short axis measurement, plays an important role in evaluation of oncological therapy responses. Selection and analysis of pathological LNs in Computed tomography (CT) images is complex task as it requires the vigilant study of CT images. Hence, in this paper, an automated method based on Convolutional neural network (CNN) has been designed to classify LNs in pathological and non-pathological groups. Large kernel sizes in CNN increases trainable parameters and hence makes it more probable for overfitting. Hence, authors have designed and implemented factorized convolution-based CNN model on machine augmented LNs dataset for identifying pathology. Four variants of convolutional filters based on various level of factorization, are designed and applied for classification of abdominal LNs. The best achieved accuracy is 96.38%.
Encrypted Steganography Quick Response Scheme for Unified Hotel Access Control System

This paper proposes a Unified Hotel Access Control (UHAC) system, which is a complete integrated hardware and software solution for contactless check-in and access to hotel room using the proposed Encrypted Steganography Quick Response (ESQR) key, either through mobile devices or physical printouts. An embedded system installed in front of hotel room was deployed to allow guest simultaneously check-in and unlock room door using the ESQR key. This eliminates the need of queuing up and checking-in at reception desk or through check-in kiosk. Thus, the proposed contactless check-in-access is more convenient and embrace social distance during the pandemic. The new approach could increase guest's satisfaction and privacy level because the ESQR key can be digitally transmitted in advance to the guest upon successful booking and payment. Additionally, a survey of 108 participants was conducted to evaluate the users' perception towards the UHAC system. The findings suggest that the UHAC system is perceived as a convenient and secure system, resulting in positive behavioral intention to use the system. Thus, the UHAC system has the potential to transcend the hotel and tourism industries by enhancing the guest staying experience through a more convenient, secure, and easy contactless check-in and access to hotel rooms.
Conditional Generative Adversarial Defogging Algorithm Based on Polarization Characteristics

To overcome image degradation under the conditions of haze, and fog, a method using conditional generative adversarial defogging algorithm based on polarization characteristic is proposed. Four original images with different polarization angles were obtained from the original image, and then the polarization images were characterized using Stokes vectors. From the relationship between Stokes vector and polarization image, each polarization image with different angles is input into the same network to extract features. At the same time, the enhanced network is used to extract the characteristics of the fog area in the polarization image, and the polarization information is extracted by layer jump connection, which is fused with the image features of different angles. By constructing the loss function and obtaining the optimal solution, the fog-free image is finally reconstructed. Experimental results show that a clear image can be reconstructed in fog situations by using conditional generative adversarial network-based on polarization characteristics, and the structural similarity is improved by about 10%, the peak signal to noise ratio is increased by about 0.5.
The evidence beyond the wall: Memory forensics in SGX environments

Software Guard eXtensions (SGX) is a hardware-based technology that introduces unobservable portions of memory, called enclaves, that physically screens software components from system tampering. Enclaves can be used to run arbitrary programs (including malicious code), but their actual impact on digital forensics and incident response remains unknown. In our work, we propose a methodical study of what information can be retrieved from an SGX machine and how to use this information to infer the enclaves interfaces and structure layout. We tested our techniques over a dataset of 45 SGX applications and we showed the practicality of our techniques in a real-product use-case and on two malware-enclaves. • Discussion of the limitation of current forensic analysis in SGX-machines. • Study of which information can be extracted from a machine running SGX enclaves. • New techniques for SGX enclaves detection, identification, and analysis.
Construction of Literacy Education Model for Contemporary College Students Based on DQR Framework

With the continuous development of the society, the society requires higher and higher standards of talent training. The Party and the state attach great importance to the cultivation of talents in colleges and universities, and have successively issued relevant policies to put forward clear requirements for the education system in colleges and universities. This paper mainly studies the construction of contemporary college students' literacy education model based on DQR framework. This paper uses the questionnaire survey method to sort out the status quo of data literacy education in China's university libraries, such as the development of the education, the subject of education, the content of education, the form of education, and the evaluation of education results, so as to understand the situation of data literacy education in China's university libraries. The problems of data literacy education in colleges and universities in China mainly focus on the following aspects: the lack of clarity of educational objectives, the lack of novelty of educational content, the singleness of educational methods and means, the lack of hierarchy and individuation of education, and the imperfection of educational achievement evaluation system.
Criminal Investigation Image Retrieval Based on Deep Hash Code

For the problem of low accuracy and efficiency in criminal investigation image retrieval tasks, proposed a retrieval method based on deep hash coding. Adopting ResNet as backbone network for feature extraction, and adding a pyramid pooling layer to enhance the quality of image features; In order to improve the retrieval efficiency, a deep hash layer is introduced to adaptively extract binary hash code for the image. By means of image-pair training method, and Combination of classification loss and contrast loss for joint learning, optimize training effect. Experiments show that the accuracy and efficiency of image retrieval by this method are significantly improved when compared with the existing benchmark algorithms.
Management System of Hybrid Classroom Teaching Process Oriented to Smart Classroom

With the continuous development of information technology, the hybrid teaching model has become a trend, and it has been widely used in the field of education in our country. This article takes the hybrid classroom oriented to the smart classroom as the research object, and conducts research from the three aspects of smart classroom, traditional classroom and teaching management, and summarizes the problems and shortcomings of the current teaching management process in colleges and universities, and elaborates the hybrid classroom oriented to the smart classroom. It has advantages in assisting college students' learning; finally, according to these situations, some improvement measures and suggestions are put forward for reference and use in teaching practice, so that Chinese universities can achieve more significant development results in information technology and subject basic knowledge.
Comparative and Development of Network Information Teaching and Traditional Teaching Methods

With the gradual enrichment of the theory and practice of information-based teaching, the proportion of network information-based teaching methods is increasing, but the traditional teaching methods are weakened. The traditional teaching method for students to master the basic knowledge and skills of the system has its effect can not be ignored, but it ignores the initiative of students as cognitive subjects, is not conducive to the formation of students' innovative development and cooperation and communication ability and creative ability. Network information teaching method will be a student-centered, comprehensive and diversified development of students, and can promote the common progress of education and teachers and students. The implementation of the two teaching methods is quite controversial, so the analysis and comparison is particularly important.
Comparative study of data preparation methods in quantum clustering algorithms

More powerful and better performing, quantum algorithms offer a noticeable speedup in comparison with classical algorithms. This is due to the superposition property of quantum information. It helps to obtain algorithms that are better in terms of speed and performance. Machine learning benefits from quantum computing advantages to help time consuming algorithms run faster in the best conditions (without having to lose information). In the clustering algorithms case, the distance calculation between data points is the most resource consuming step. Thus, the use of a quantum distance is very helpful. In this paper, we test a number of quantum clustering algorithms with different data preparation methods. Experiments were done for different datasets (Iris, Wine, Breast cancer) and the comparison is based on the Clustering quality and the accuracy score.
A Novel Subpixel Size Calibration Method for the Size Detection of Microtarget on Large-Aperture Optics Surface

Large-aperture optics play key roles in the laser beam transmission processes involved in high-power laser facilities. The microtargets (e.g., contaminants, dust, and particles) adhering to the optical surfaces would greatly affect the optical performance and, thus, need to be accurately detected for evaluating the cleanliness quality of optical components. However, due to the limit of camera resolution (the actual size of the target area on the optics surface represented by a single pixel ranges from 50 to 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> ), it is of great challenge to accurately detect the sizes of tiny microtargets (roughly 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> ). In this work, a novel subpixel size calibration method based on the regression model and the Mie scattering theory was proposed to precisely calculate the actual sizes of the tiny microtargets. The least-squares support vector machine (LSSVM) principle was applied to establish the area calibration model, and the random sampling consistency (RANSAC) algorithm was applied to optimize the selection of training samples and eliminate the outliers at the same time. The results showed that the relative diameter errors of about 90% of the detected microtargets were less than 30%, which is much better than that of the common pixel-level calibration method. The minimum detectable diameter of the microtargets with the proposed size calibration method can reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.8~\mu \text{m}$ </tex-math></inline-formula> , which is much smaller than the resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$53.7~\mu \text{m}$ </tex-math></inline-formula> ) of commercial cameras. A similar high calibration accuracy can be achieved in different regions on the optical surfaces although the illumination conditions were different. The proposed subpixel size calibration method can be applied to detect the microtargets with dimensions as small as 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> on the large-aperture reflector surfaces, which would greatly save the cost of detection equipment and improve the detection efficiency.
Parallel dynamic topic modeling via evolving topic adjustment and term weighting scheme

The parallel Hierarchical Dirichlet Process (pHDP) is an efficient topic model which explores the equivalence of the generation process between Hierarchical Dirichlet Process (HDP) and Gamma-Gamma-Poisson Process (G2PP), in order to achieve parallelism at the topic level. Unfortunately, pHDP loses the non-parametric feature of HDP, i.e., the number of topics in pHDP is predetermined and fixed. Furthermore, under the bootstrap structure of pHDP, the topic-indiscriminate words are of high probabilities to be assigned to different topics, resulting in poor qualities of the extracted topics. To achieve parallelism without sacrificing the non-parametric feature of HDP, in addition to improve the quality of extracted topics, we propose a parallel dynamic topic model by developing an adjustment mechanism of evolving topics and reducing the sampling probabilities of topic-indiscriminate words. Both supervised and unsupervised experiments on benchmark datasets show the competitive performance of our model.
Multilingual Short Text Analysis of Twitter Using Random Forest Approach

In this digital era language recognition of text plays an important role in the fields like information retrieval systems. Language recognition makes such systems capable of handling multilingual queries for which relevant documents are fetched according to their respective language. It also helps in retrieving information from multilingual sites such as Twitter. Existing work in language identification mainly focuses on large text. This works addresses the problem of language recognition of short text. The work employs two machine learning approaches based on n-gram representation of text - Random Forest and Weighted Ensemble learning. The study performed over 4 popular languages (English, Spanish, French, and German) reveals that Random Forest Algorithm outperforms Naive Bayes, Logistic Classifier and Weighted Ensemble approaches by up to 33.
Digital Image Soil Analysis based on Machine Learning

In this paper, a digital image soil analysis model based on machine learning is established.According to the mean value of HSV and image foreground, two algorithms, MLP and SVM, were used to predict the drug content in the same soil, which proved the accuracy of image analysis by MLP network and support vector machine. Drug content detection by image can be applied to land management, which provides a new idea and effective reference for comprehensive soil analysis in many aspects.
ML-based data classification and data aggregation on the edge

This study focuses on sensor classification using machine learning algorithms, to improve data aggregation on the Edge. This aspect is particularly important in large-scale Internet of Things environments, where data aggregation derived from sensors from different vendors often requires human intervention. The proposed research is focused on relying on machine learning to classify sensors based on their semantic descriptions.
PicArrange - Visually Sort, Search, and Explore Private Images on a Mac Computer

The native macOS application PicArrange integrates state-of-the-art image sorting and similarity search to enable users to get a better overview of their images. Many file and image management features have been added to make it a tool that addresses a full image management workflow. A modification of the Self Sorting Map algorithm enables a list-like image arrangement without loosing the visual sorting. Efficient calculation and storage of visual features as well as the use of many macOS APIs result in an application that is fluid to use.
CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes

Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.
Introduction to Semi-Classical Analysis for Digital Errors of Qubit in Quantum Processor

In recent years, remarkable progress has been achieved in the development of quantum computers. For further development, it is important to clarify properties of errors by quantum noise and environment noise. However, when the system scale of quantum processors is expanded, it has been pointed out that a new type of quantum error, such as nonlinear error, appears. It is not clear how to handle such new effects in information theory. First of all, one should make the characteristics of the error probability of qubits clear as communication channel error models in information theory. The purpose of this paper is to survey the progress for modeling the quantum noise effects that information theorists are likely to face in the future, to cope with such nontrivial errors mentioned above. This paper explains a channel error model to represent strange properties of error probability due to new quantum noise. By this model, specific examples on the features of error probability caused by, for example, quantum recurrence effects, collective relaxation, and external force, are given. As a result, it is possible to understand the meaning of strange features of error probability that do not exist in classical information theory without going through complex physical phenomena.
Hyperparameter Learning of Stochastic Image Generative Models with Bayesian Hierarchical Modeling and Its Effect on Lossless Image Coding

Explicit assumption of stochastic data generative models is a remarkable feature of lossless compression of general data in information theory. However, current lossless image coding mostly focus on coding procedures without explicit assumption of the stochastic generative model. Therefore, we have difficulty discussing the theoretical optimality of the coding procedure to the stochastic generative model. In this paper, we solve this difficulty by constructing a stochastic generative model by interpreting the previous coding procedure from another perspective. An important problem of our approach is how to learn the hyperparameters of the stochastic generative model because the optimality of our coding algorithm is guaranteed only asymptotically and the hyperparameter setting still affects the expected code length for finite length data. For this problem, we use Bayesian hierarchical modeling and confirm its effect by numerical experiments. In lossless image coding, this is the first study assuming such an explicit stochastic generative model and learning its hyperparameters, to the best of our knowledge.