Reviewerly/exHarmony huggingface dataset:
Teacher Education and Management: Innovative Application of Ecological Management System of Big Data Management System

At present, with the rapid growth of China’s comprehensive national power, China wants to accelerate the construction of world-class universities and world-class disciplines to match it. Therefore, the education and management of university teachers is crucial, and the current research results at home and abroad in this area are remarkable. In this paper, we will start from the innovative application of university teachers’ management system and use the Oracle and SQL Server database processors commonly used in big data processing to process data. It is concluded that China’s teaching force is gradually balanced; the comprehensive quality of the teaching force is improving; the academic atmosphere is more open; the fields and perspectives of academic research are richer; the distribution of teachers at all levels in China is more balanced; the comprehensive quality of our teachers is improving; the system of education and scientific research in China’s universities is improving; and the ecological management system of universities is improving. In addition, in view of the current situation of education and management of teachers in China’s colleges and universities, according to the results of big data analysis, we also propose (1) to improve the academic level and establish a strict institutional threshold for the college teaching profession; (2) to effectively implement the policy of freedom and equality in academic academia and improve the appointment and evaluation system of teachers and principals in higher education institutions; (3) to insist on implementing the principle of academic fairness and optimizing the teacher evaluation system; (4) to innovate and implement the incentive mechanism for teachers in colleges and universities according to the talent training characteristics of teachers’ academic career ability; and (5) to establish the access system of college teachers’ career development and promote academic career development and other suggestions of big data management for college teachers.
Security analysis for a mutually partially unbiased bases–based protocol

The recently proposed mutually partially unbiased bases (MPUB)–based protocol, which encodes with Laguerre–Gaussian modes and Hermite–Gaussian modes of the same mode order, can close the security loophole caused by state-dependent diffraction. However, its pessimistic security proof limits the performance, and some practical issues, such as finite-key size and imperfect sources, have not been considered. Here, we improve the key rates of the MPUB-based protocol by accurately estimating the phase error rate. Moreover, the effect of finite-key size and its performance when combined with the decoy state method are demonstrated. Our work broadens the application scope of the MPUB-based protocol, and thereby advances the development of high-dimensional quantum key distribution using spatial modes.
Post-quantum Signature Scheme to Secure Medical Data

Quantum computers are considered to have the ability to change the landscape of data security in near future. In recent years, progress on this matter showed us that quantum computers can solve certain computational problems significantly faster than classical computers. Some firms or organizations claim to have constructed the world’s most powerful quantum computer, which is far more powerful than today’s classical computers. So, the concerning fact is that, the most secure ledger, blockchain now is facing critical issues of privacy and security. Cryptographic hash is one of the strongest tools in the cryptographic arena that is used in blockchain applications for confidentiality. However, cryptographic hash is certainly breakable under quantum computing because of Grover’s and Shor’s algorithm. To make a secure blockchain for sensitive medical data, we have to move into post-quantum blockchain. Under these circumstances, the proposed signature scheme is based on the combination of two SHA-3 finalists (Keccak-512 and Skein-512) and Merkle tree which will be suitable for blockchain technology. By exploiting the structure and properties of sponge function, Skein-512, and Merkle tree, the proposed scheme can achieve a certain benchmark of security and flexibility. Moreover, the proposed signature scheme can easily adopt the existing blockchain applications under the cloud environment.
Investigating the relationships between dialog patterns and user satisfaction in customer service chat systems based on chat log analysis

While previous studies of customer service chat systems (CSCS) understood user satisfaction as individuals’ subjective perceptions and depended heavily on self-report methods for satisfaction measurement, this article presents an obtrusive chat log analysis that followed the established approaches of search log analysis and examined the relationships between dialog patterns and user satisfaction. An 81-day chat log was obtained from a real-world CSCS that involves both a chatbot and human representatives. A total of 75,918 chat sessions/147,972 sub-sessions containing 251,556 user messages and 349,416 system messages were extracted after data processing and analysed in terms of topic, length and path. As found in this study, the users were more likely to get satisfied on low-difficulty topics. The dialog between the CSCS and users was shallow in general. While human representatives’ elaboration contributed to user satisfaction, the chatbot was responsible for damaging user satisfaction. The significance of this study consists not only in providing objective evidence about user satisfaction in online chat but also in generating practical implications for the CSCS to improve user satisfaction.
The Role of Public Education Schools in Developing Social Responsibility in the Light of Contemporary Global Trends

The study aimed at recognizing the reality of the role of public education schools in developing social responsibility (which includes the following responsibility dimensions: towards the self, towards family, towards friends, towards society, and the dimension of loyalty and national responsibility). The important obstacles were specified, and activation mechanisms in the light of contemporary global trends were identified. The study adopted the analytical descriptive approach, with a random sample being selected from the population of the study, and a questionnaire used for data gathering. The results of the study demonstrated that the reality of the role of public education schools in developing social responsibility was in the range of “strongly agree”. The dimension of loyalty and national responsibility ranked first, and its highest-scoring response was the female teachers’ continuous care for their students’ acquirement of the importance of national unity among all individuals in society. The dimension of responsibility towards friends came at the fifth and final rank, and its highest response was the teachers’ demonstration of bad company effect on the students concerning their values and ideas. The study provided many recommendations.
Arithmetic correlation of binary half‐<i>ℓ</i>‐sequences

Abstract The arithmetic correlations of two binary half‐ ℓ ‐sequences with connection integer p r , which is an odd prime power, are investigated. Possible values (of the arithmetic correlation) are calculated. In particular, if p ≡ 1 (mod 8), the authors prove that they are zero for non‐trivial shifts, that is, the half‐ ℓ ‐sequences have ideal arithmetic correlations. If p ≡ −1 (mod 8), an upper bound, which is of order of magnitude p r −1/2 ln p , is derived by using earlier results on the imbalance of half‐ ℓ ‐sequences with connection integer p studied by Gu and Klapper and later improved by Wang and Tan.
Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

Stereo confidence estimation aims to estimate the reliability of the estimated disparity by stereo matching. Different from the previous methods that exploit the limited input modality, we present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networks (LAF-Net), learns locally-varying attention and scale maps to fuse the tri-modal confidence features. Moreover, we propose a knowledge distillation framework to learn more compact confidence estimation networks as student networks. By transferring the knowledge from LAF-Net as teacher networks, the student networks that solely take as input a disparity can achieve comparable performance. To transfer more informative knowledge, we also propose a module to learn the locally-varying temperature in a softmax function. We further extend this framework to a multiview scenario. Experimental results show that LAF-Net and its variations outperform the state-of-the-art stereo confidence methods on various benchmarks.
Clustering Algorithm in English Language Learning Pattern Matching under Big Data Framework

The Internet era has brought new challenges and opportunities for English learning and English teaching. At the same time, basic education is fully implementing quality education and respecting students’ individual differences. The same teacher teaches the same content to the same class of students, but some students perform well, and some students perform poorly due to the influence of intellectual and nonintellectual factors. The uneven performance of students in the same class makes it very difficult for teachers to teach. In view of the current situation of university English teaching and the trend of respecting students’ individual development in the new era, this study investigates the basic concept of English language learning pattern matching, its main features, and practical application in the process of university English teaching. The clustering algorithm based on the big data framework is proposed for English language learning pattern matching, which is fault-tolerant and can quickly acquire and process the big data information in English teaching. By analyzing the characteristics of the data mining method of students’ English learning behavior, the method of clustering processing for students’ English learning data mining and the processing method of students’ English learning clustering data are explored. The method is highly adaptable and can be used for actual English language learning pattern matching, and actively explores the main path of English teaching change and innovation.
Bayesian semiparametric approach to quantile nonlinear dynamic factor analysis models with mixed ordered and nonignorable missing data

In classical nonlinear dynamic factor analysis models (NDFAMs), we assume manifest variables follow the normal distribution. However, in some applications, the normality assumption may be unreasonable or questionable. This paper proposes a quantile NDFAM in which manifest variables may be observed or unobserved but associated with the observed ordinal indicators or subject to missingness, and develops a Bayesian semiparametric method to estimate unknown parameters and latent variables by utilizing the Dirichlet process prior to approximate unknown distribution of random effects. Logistic and probit models are adopted to specify propensity scores for missing manifests and covariates, respectively. An exponential family distribution is assumed for missing covariates. Bayesian local influence is presented to assess the effect of minor perturbations to data, priors, and sampling distributions. Several simulation studies and a real example are illustrated by the proposed methodologies.
Novel Optimization Approach for Camera Calibration in Traffic Scenes

Currently, most camera calibration methods for traffic scenes are based on vanishing points and road geometry markings with simplified camera models, which can only be applied to scenes containing straight roads. However, in practical applications, cameras are usually installed with roll angles and scenes containing curved roads, to which existing methods are not applicable. To solve the above problems, we propose a novel optimization approach for camera calibration in traffic scenes, which can be applied to curved road scenes and predict camera roll angle. Firstly, a camera space model with a camera roll angle is established for image rotation. Secondly, vehicle trajectories are extracted for the best vanishing point by a parallel coordinate system and diamond space. Vehicle trajectories are also used to obtain calibration regions for extracting road markings and edges. The road markings, edges, and the best vanishing point obtained by the above two steps automatically are more accurate and stable, especially for curved road scenes. Based on the road markings and the best vanishing point, initial calibration can be conducted. Finally, by extracting redundant markings in the calibration region, the non-linear constraint of redundant markings on the road is proposed to obtain optimized calibration parameters and predict the camera roll angle. Through experimental validation on the public dataset BrnoCompSpeed and highway scenes, the proposed approach can achieve better calibration results in both straight and curved road scenes with the mean calibration error reduced by 30% compared with the previous calibration methods.
Towards blind detection of steganography in low‐bit‐rate speech streams

To prevent the abuse of low-rate speech-based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low-bit-rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self-paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under-sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under-sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low-bit-rate speech-based steganography, and the steganalysis scheme based on the self-paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self-paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self-paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.
Building 21st Century Skills Through Technology in General Music Classes

In today’s global community, 21st century skills are essential for all students. In addition, in a digital world, technology is expected to be part of students’ educational experiences. In this article, I provide practical strategies and ideas for incorporating technology into general music curriculum to promote students’ 21st century skills. In particular, I offer specific practices that utilize technology to meet 21st century skills—creativity, critical thinking, collaboration, communication, global awareness, and technology literacy.
Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models

Digital Whole Slide Imaging (WSI) systems allow scanning complete probes at microscopic resolutions, making image compression inevitable to reduce storage costs. While lossy image compression is readily incorporated in proprietary file formats as well as the open DICOM format for WSI, its impact on deep-learning algorithms is largely unknown. We compare the performance of several deep learning classification architectures on different datasets using a wide range and different combinations of compression ratios during training and inference. We use ImageNet pre-trained models, which is commonly applied in computational pathology. With this work, we present a quantitative assessment on the effects of repeated lossy JPEG compression for ImageNet pre-trained models. We show adverse effects for a classification task, when certain quality factors are combined during training and inference.
A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology

Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. The cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. For diagnostic work-up, pathologists first locate glands in the whole biopsy core, and—if they detect cancer—they assign a Gleason grade. This time-consuming process is subject to errors and significant inter-observer variability, despite strict diagnostic criteria. This paper proposes an automated workflow that follows pathologists' modus operandi, isolating and classifying multi-scale patches of individual glands in whole slide images (WSI) of biopsy tissues using distinct steps: (1) two fully convolutional networks segment epithelium versus stroma and gland boundaries, respectively; (2) a classifier network separates benign from cancer glands at high magnification; and (3) an additional classifier predicts the grade of each cancer gland at low magnification. Altogether, this process provides a gland-specific approach for prostate cancer grading that we compare against other machine-learning-based grading methods.
Development of E-Module Based on the Traditional Puyuh Game on the Cooperation Character and the Tolerance of Elementary School Students

Electronic modules provide convenience for teachers and students in learning activities. This study aims to determine the feasibility or validity of the electronic module based on the traditional game "Puyuh" in learning activities and to determine the effect of implementing the electronic module on the character of cooperation and tolerance in students. The type of research used is development with a development model, namely 4D (Define, Design, Develop, and Disseminate). The sample of this study amounted to 50 students consisting of two classes. The data collection instruments are questionnaires and interviews. The data analysis used is descriptive qualitative and quantitative. The research results obtained are the electronic module based on the traditional game "Puyuh" which is feasible or valid to be used in learning activities. Electronic modules are an alternative in learning activities for teachers and students. The implementation of the electronic module based on the traditional game "Puyuh" has an influence on the character of cooperation and tolerance in students, besides that it can also introduce technology and preserve local wisdom in students.
Deep Multimodal Guidance for Medical Image Classification

Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference. We make our code and trained models available at: https://github.com/mayurmallya/DeepGuide .
DSP-Net: Deeply-Supervised Pseudo-Siamese Network for Dynamic Angiographic Image Matching

During percutaneous coronary intervention (PCI), severe elastic deformation of coronary arteries caused by cardiac movement is a serious disturbance to physicians. It increases the difficulty of estimating the relative position between interventional instruments and vessels, leading to inaccurate operation and higher intraoperative mortality. Providing doctors with dynamic angiographic images can be helpful. However, it often faces the challenges of indistinguishable features between consecutive frames and multiple modalities caused by individual differences. In this paper a novel deeply-supervised pseudo-siamese network (DSP-Net) is developed to solve the problem. A pseudo siamese attention dense (PSAD) block is designed to extract salient features from X-ray images with noisy background, and the deep supervision architecture is integrated to accelerate convergence. Evaluations are conducted on the CVM X-ray Database built by us, which consists of 51 sequences, showing that the proposed network can not only achieve state-of-the-art matching performance of 3.48 Hausdorff distance and 84.09% guidewire recall rate, but also demonstrate the great generality to images with different heart structures or fluoroscopic angles. Exhaustive experiment results indicate that our DSP-Net has the potential to assist doctors to overcome the visual misjudgment caused by the elastic deformation of the arteries and achieve safer procedure.