Reviewerly/exHarmony huggingface dataset:
Research on Training of Scientific and Technological Innovation Talent

Scientific and technological innovation talents training" should not only implement the strategy of rejuvenating the country through science and education, but also adhere to and improve the strategy of strengthening the country through talents.Since the 18th National Congress of the CPC, in various conferences, reports, speeches and theoretical documents, important discussions have been made by President Xi on "why to train scientific and technological innovation talents, what kind of scientific and technological innovation talents to train, and how to train scientific and technological innovation talents".President Xi's important viewpoints about scientific and technological innovation talent training have distinctive characteristics of the times, rigorous scientific characteristics and Lofty people characteristics, and its value is embodied in three aspects: enriching and developing the Marxist scientific and technological innovation thought, planning the action guide of China's "scientific and technological innovation talent training", and contributing to the Chinese strategy of "solving the common problems facing mankind".
The Application of Meta-cognitive Strategies in English Reading Teaching in Senior High School

Under the requirements of the new curriculum standard for the cultivation of senior high school students' reading ability, this paper mainly discusses the application of meta-cognitive strategies in English reading teaching in senior high school.The author will take the reading class, 'A Day in the Cloud' in Reading and Thinking part of Wildlife Protection in the second unit of New PEP Senior High School English Compulsory II as an example.Combining meta-cognitive strategies with reading teaching process can improve students' autonomous learning ability and master the reading strategies; on the other hand, it can provide corresponding measures for senior high school English teachers and improve their ability of using meta-cognitive strategies in English teaching.
Research on the Strategy of Integration of General Education and Professional Education in Finance under the Background of New Liberal Arts

Realizing the integration of general education and professional education in finance is the key to cultivate a composite and innovative finance major in the context of the new liberal arts.In view of the current problems such as unclear talent training objectives, imperfect teaching system, and weak allocation of teachers, this paper puts forward strategies for the integration of general education and professional education in finance to promote finance from adapting to service to supporting and leading, from subject orientation to demand orientation, and from specialty segmentation to cross integration.It determines the talent training goal of "localization + international vision", creates a teaching system of "theory talent practice" coupling, establishes a teaching team of interdisciplinary, knowledge sharing and collaborative innovation, promotes the upgrading of traditional finance majors, and cultivates compound and innovative financial professionals who adapt to the development of new era and new financial formats.
Research on the Consistency between English Curriculum Standards and Reading Questions under the Background of the New College Entrance Examination

Under the background of the new curriculum standard and the new college entrance examination reform, the consistency between the two has become one of the core issues of English educators."SEC" consistency analysis mode is used to construct a consistency analysis framework.The consistency of the new curriculum standard and the new college entrance examination paper I from 2020 to 2022 is analyzed from the dimensions of overall consistency, cognitive level and content theme.The results show that, the consistency between the two is high on the whole; The three sets of test papers have the same focus on the cognitive level, but the scope is not comprehensive and the proportion is unbalanced; The focus of the three sets of test papers in the content theme is the same, but the proportion is unbalanced.Therefore, it is suggested that the English reading proposition of the college entrance examination should be based on the curriculum standard and balance the proportions of both cognitive level and content theme; English teachers in senior high school should study the curriculum standard, and the reading questions of the college entrance examination, to clarify the cognitive level and content theme requirements, then adjust the direction of reading teaching.
Hyperspectral Image Mixed Noise Removal via Nonlinear Transform-Based Block-Term Tensor Decomposition

Recently, block-term decomposition with rank-( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L<sub>r</sub></i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L<sub>r</sub></i> ,1) (termed as LL1 decomposition), which is physically inspired by linear spectral unmixing, has received increasing attention in hyperspectral images (HSIs) denoising. However, due to the intrinsic nonlinear structure of real-world HSIs, the low-rankness of HSIs is usually implicit. Moreover, the essential uniqueness guarantee is usually violated with the low-rank assumption of the abundance maps unsupported in real scenarios, which hampers the successful deployment of LL1 decomposition. Inspired by the nonlinear spectral unmixing, we propose a nonlinear learnable transform-based LL1 decomposition (NT-LL1) for characterizing the implicit low-rank structure of real-world HSIs. More concretely, the nonlinear learnable transform in NT-LL1 decomposition is a composed transform consisting of a linear semi-orthogonal transform and a component-wise nonlinear transform, which collaboratively enhances the low-rankness of the abundance maps. Empowering with the NT-LL1 decomposition, we propose an NT-LL1 decomposition-based model for HSIs denoising. To tackle the resulting model, we develop an efficient proximal alternating minimization-based algorithm with a convergence guarantee. Extensive experimental results including simulated and real data collectively verify the superiority of the proposed method as compared with the competing methods.
FUZZILLI: Fuzzing for JavaScript JIT Compiler Vulnerabilities

JavaScript has become an essential part of the Internet infrastructure, and today's interactive web applications would be inconceivable without this programming language.On the downside, this interactivity implies that web applications rely on an ever-increasing amount of computationally intensive JavaScript code, which burdens the JavaScript engine responsible for efficiently executing the code.To meet these rising performance demands, modern JavaScript engines ship with sophisticated just-in-time (JIT) compilers.However, JIT compilers are a complex technology and, consequently, provide a broad attack surface for potential faults that might even be security-critical.Previous work on discovering software faults in JavaScript engines found many vulnerabilities, often using fuzz testing.Unfortunately, these fuzzing approaches are not designed to generate source code that actually triggers JIT semantics.Consequently, JIT vulnerabilities are unlikely to be discovered by existing methods.In this paper, we close this gap and present the first fuzzer that focuses on JIT vulnerabilities.More specifically, we present the design and implementation of an intermediate representation (IR) that focuses on discovering JIT compiler vulnerabilities.We implemented a complete prototype of the proposed approach and evaluated our fuzzer over a period of six months.In total, we discovered 17 confirmed security vulnerabilities.Our results show that targeted JIT fuzzing is possible and a dangerously neglected gap in fuzzing coverage for JavaScript engines.
Genetic Algorithm of GIS in Water Pollution Control Planning

Water is the source of life.The shortage of water resources and environmental pollution have become the bottleneck restricting economic development.The main pollutants are organic substances, such as sewage nitrogen, biochemical oxygen demand, permanganate index, volatile phenol, etc.These factors affect a wide range of areas and have a high degree of harm.In order to make full use of water resources and reduce water pollution (WP for short here), it is necessary to treat water environment as a whole to make it meet the water quality standards.In this paper, GIS (Geographic Information Science) technology is used for comprehensive evaluation of water quality, and certain mathematical calculation methods are used to achieve quantitative and objective evaluation of water quality, so as to achieve the objective evaluation of water quality and work efficiency.Genetic algorithm (GA) is an adaptive global optimal probability search method, which imitates the genetic and evolutionary process of organisms in nature.In this paper, from the perspective of biological genetics, a GA for directly coding parameters is designed, which solves the problem that traditional nonlinear problems are easy to fall into local optimum.At present, it has been widely used in many aspects.Based on GA of GIS, this paper applies it to WP control planning.The study found that the cost of WP control system based on GA based on GIS was reduced by 30.32 million compared with the cost of WP control before improvement.
An Immersive Learning System with Multimodal Cognitive Processes Inference

Using multimodal technology to infer learners’ cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner’s cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner’s actions, emotions and attention to infer learner’s cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios.
TextFourierNet: Arbitrary-shaped Scene Text Detection Based on Fourier Contour Modeling

Arbitrary-shaped scene text detection is a challenging task as the network can’t accurately learn the geometric layout of the texts such as curve shapes, various scales and random rotations. Most of the advanced methods address this problem through pixel-level classification. However, the classification-based approaches might lead to costly post-processing and fail to separate adjacent scene text instances. To tackle this issue, we propose a novel arbitrary-shaped scene text detector named TextFourierNet, which is a regression-based method. We model arbitrary-shaped scene text instances in polar coordinates and encode the complex geometric layouts into simple Fourier coefficients. TextFourierNet reconstructs text boundaries via an Inverse Fourier Transformation (IFT) and one NMS post-processing. To effectively separate adjacent texts and learn boundary information, we represent a text region as four sub-regions, which greatly improves the overall performance. Extensive experiments on two public datasets show that TextFourierNet is effective for arbitrary-shaped scene text detection.
Patch Elastic Deformation: An Effective Data Augmentation Method

In image detection, various models and data enhancement algorithms have emerged in recent years. But these methods are either random copy-paste or rotation but seldom consider the local deformation caused by the partial extrusion of the object. Elastic deformation exists widely in the real world. Patch Elastic Deformation simulates the shape changes of things after various reality effects. As a complement to existing data augmentation methods, elastic deformation may appear anywhere in the sample, increasing the richness of the selection. Specifically, we randomly select a rectangle patch in the image and then apply elastic deformation to it, forcing models to extract input images’ essential features. For image classification and object detection tasks, we verify the effectiveness of our algorithm on CIFAR-10, CIFAR-100, and PASCAL VOC datasets. And we also compared it with the random erasing algorithm, proving that our algorithm can boost the performance of models.
Deep Photographic Steganography for Display-to-Camera Communication

Intricate and diverse image distortions exist in display-to-camera communication, a technology that displays steganographic images on a screen, then decodes convert messages by a camera. Modeling these distortions has always been a difficult problem. Most of the previous work is to use prior knowledge to artificially design the noise layer to approximate the real situation, however, it is of less authenticity. In this paper a more impeccable channel distortion model based on generative adversarial network, named D2C-Distortion is established to simulate the display-to-camera transmission channel caused by complex image distortions, based on which, a complete set of end-to-end photographic steganography scheme consisting of encoding, channel distortion and decoding networks are proposed. Experimental results show that the scheme with D2C-Distortion process is more robust against display-to-camera distortions compared with the one without it, demonstrating the effectiveness of the proposed distortion model.
An Ant-based Optimizer for the Training of Convolutional Neural Networks

The stochastic gradient descent algorithm is widely applied in the training of deep neural networks, however, it is poor in global search and easily falling into saddle points and local optimum. Some improved approaches, e.g. momentum-based and adaptive learning-rate, are devised to tackle these issues, but they are still not satisfactory in term of stability. To deal with these drawbacks, we propose a new optimizer for the training of convolutional neural networks by incoporating the idea of ant colony optimization (ACO) into the gradient descent methods. In this approach, each sample batch is related to an ant, and the ants cooperate to increase the diversity of optimized parameters, thus to jump of local optima. The classical optimizers such as SGD, Momentum, Adam, and their versions in the ACO framework are benchmarked with the MNIST and Fashion-MNIST datasets. The experimental results show that the optimizers with the ACO mechanism can improve the accuracy and stability of the training process. Furthermore, the algorithm is designed iteratively, thus to minimize computational overhead.
System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

With rapid progress of artificial intelligence (AI) , it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures; 2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents.
Research on Green Supply Chain Decision-Making Based on Blockchain to Eliminate the Risk of Energy Efficiency Data Fraud

Abstract: Falsification of product energy efficiency data affects the accuracy of carbon footprint tracking in green supply chain, and is not conducive to the achievement of carbon neutrality goals. Therefore, applying blockchain technology to eliminate the risk of fraudulent product energy efficiency data in the green supply chain, and then investigating its impacts on green supply chain decision-making is a new proposition worth studying. By constructing a Stackelberg game model including a manufacturer and a retailer, this paper analyzes the impacts of applying blockchain technology on green supply chain pricing decisions, carbon emission reduction and profit. We find that the increased risk of product energy efficiency data fraud will have negative impacts on product wholesale price, selling price, carbon emission reduction, and order quantity; Furthermore, the improvement of brand credibility brought about by the application of blockchain is conducive to enhancing the level of carbon emission reduction, and has essential impact on the wholesale price and the selling price, while the impacts of the marginal use cost of blockchain are the opposite. Finally, the triple factors including fixed cost, marginal usage cost and degree of brand credibility improvement jointly affect the profits of enterprises when applying blockchain. Our results have important managerial implications for government and companies who intend to improve their financial and environmental performance by applying blockchain technology in green supply chain.
A Study on the Impact of Motivational Benefits on Employee Productivity in Public Sector

The most valuable asset of every group is its ability to court process delight human resources.They serve as the foundation of every running facility.A company's ability to effectively manage its staff and retain quality employees is correlated with its ability to: recognize the contribution of each and every individual Motivation is one of those factors that influence our actions and artwork.This study was conducted to determine the effect of praise and acknowledgment, joy with supervision and focuses on the enjoyment of the process.This analysis's results demonstrate the relationship between employee motivation and performance is significant and beneficial.Next, the motivation of artists was also examined.
A Model-Driven Approach for the Management and Enforcement of Coding Conventions

Coding conventions are a means to improve the reliability of software systems, and they are especially useful to avoid the introduction of known bugs or security flaws. However, coding rules typically come in the form of text written in natural language, which makes them hard to manage and to enforce. Following the model-driven engineering principles, in this paper we propose an approach for the management and enforcement of coding conventions using structured models. We define the Coding Conventions Specification Language (CCSL), a language to define coding rules as structured specifications, from which checkers are derived automatically by code generation. To evaluate our approach, we run a thorough experiment on 8 real open-source projects and 77 coding rules for the Java language, comparing the violations identified by our checkers with those reported by the PMD static analysis tool. The obtained results are promising and confirm the feasibility of the approach. The experiment also revealed that textual coding rules rarely document all the necessary information to write a reliable checker.
Computer Aided Diagnosis for Cervical Cancer Screening using Monarch Butterfly Optimization with Deep Learning Model

Cervical cancer (CC) is the commonest gynecologic malignancy around the world. Since CC is an extremely preventable disease, thus, earlier screening signifies an efficient strategy for minimizing the worldwide problem of CC. But as a result of extremely costly procedures in developing countries, scarce awareness, and access lack to medicinal centers, the vulnerable patient population could not afford to endure examination frequently. Therefore, this study presents a Computer Aided Diagnosis for Cervical Cancer Screening using Monarch Butterfly optimization with DeepLearning (CADCCSMBODL) model. The presented CADCCS -MBODL technique employs transferlearning with hyperparameter tuning strategies for CC classification. To achieve this, the presented CADCCSMBODLtechnique employs adaptive filtering (AF) with saliency based segmentation approach. For feature extraction, the presented CADCCS-MBODL technique employs EfficientNet model with MBO algorithm as a hyperparameter optimizer. Finally, extreme gradient boosting (XGBoost) classifier is applied for classification anddetection of CC. The simulation outcome of the CADCCS-MBODL technique was tested using benchmark medical database and the outcomes signified the improved outcomes of the CADCCS-MBODL system over other existing models.
A Semi-Parametric Method for Text-to-Image Synthesis from Prior Knowledge

Text-to-image synthesis adopts only text descriptions as input to generate consistent images which should have high visual quality and be semantically aligned with the input text. Compared to images, the textual semantics is ambiguous and sparse, which makes it challenging to map features directly and accurately from text space to image space. To address this issue, the intuitive method is to construct an intermediate space connecting text and image. Using layout as a bridge between text and image not only mitigates the difficulty of the task, but also constrains the spatial distribution of objects in the generated images, which is crucial to the quality of synthesized images. In this paper, we build a two-stage framework for text-to-image synthesis, i.e., Layout Searching by Text Matching, and Layout-to-Image Synthesis with Fine-Grained Textual Semantic Injection. Specifically, we build the prior layout knowledge from the training dataset and propose a semi-parametric layout searching strategy to retrieve the layout that matches the input sentence by measuring the semantic distance between different textual descriptions. In the stage of layout-to-image synthesis, we construct the Textual and Spatial Alignment Generative Adversarial Networks (TSAGANs) that are designed to guarantee the fine-grained alignment of the generated images with the input text and layout obtained in the first stage. Extensive experiments conducted on the COCO-stuff dataset manifest that our method can obtain more reasonable layouts and improve the performance of synthesized images significantly.