Reviewerly/exHarmony huggingface dataset:
Linear Cryptanalysis and Its Variants with Fast Fourier Transformation Technique on MPC/FHE/ZK-Friendly $$\mathbb {F}_p$$-Based Ciphers

The emergence of advanced cryptographic protocols has promoted the developments of many applications, such as secure multi-party computation (MPC). For this reason, new symmetric-key primitives have been designed to natively support the finite field $$\mathbb {F}_p$$ with odd characteristic for better efficiencies. However, some well-studied symmetric cryptanalytic methods and techniques over $$\mathbb {F}_2^n$$ cannot be applied to these new primitives over $$\mathbb {F}_p$$ directly. Considering less standard design approaches adopted in these novel MPC-friendly ciphers, these proposals are in urgent need of full investigations; generalizations of the traditional cryptanalytic tools and techniques to $$\mathbb {F}_p$$ will also contribute to better understand the security of these new designs. In this paper, we first show that the Fast Fourier Transform (FFT) technique for the estimations of correlation, introduced by Collard et al. at ICISC 2007, can be applied to $$\mathbb {F}_p$$ and significantly improves the complexity of Matsui’s Algorithm 2 over $$\mathbb {F}_p$$ . Then, we formalize the differential-linear (DL) cryptanalysis to $$\mathbb {F}_p$$ . Inspired by the differential-linear connectivity table (DLCT) introduced by Bar-On et al. at EUROCRYPT 2019, we also include the DLCT into the consideration, and find the relation between DLCT and differential distribution table (DDT) over $$\mathbb {F}_p$$ . Finally, we mount key recovery attacks on a version of HADESMiMC, which is a SHARK-like MPC-friendly block cipher proposed by Grassi et al. at EUROCRYPT 2020. We denote this version as HADESMiMC-128 in this paper. For linear cryptanalysis with the FFT technique, we can attack 7 rounds of HADESMiMC-128. For DL cryptanalysis, a 7-round key recovery attack of HADESMiMC-128 is also mounted but with better time and data complexity. It should be noted that the attacks are still far from threatening the security of the full 14-round HADESMiMC-128.
Time Series Identification Using Monte Carlo Method

This paper focuses on time series (namely autoregressive, moving-average, and autoregressive-moving-average) models identification using one of the most universal Monte Carlo method, the Metropolis-Hasting algorithm. Theoretical formulation of a problem as well as some identification results are given.
Analysis of small target detection algorithm based on SSD and YOLOv5

SSD is a single-stage target detection algorithm, which performs feature extraction by convolutional neural network and takes different feature layers for detection output, so SSD is a multiscale detection method.In the feature layer to be detected, a 3*3 convolution is directly used to perform the transformation of the channels.ssd uses an anchor strategy with pre-defined anchors of different aspect ratios, and each output feature layer predicts multiple detection frames (4 or 6) based on the anchor.A multi-scale detection approach is used, where a shallow layer is used to detect small targets and a deep layer is used to detect large targets.yolov5 is a single-stage target detection algorithm, which adds some new and improved ideas to yolov4, resulting in a significant performance improvement in both speed and accuracy.We conduct algorithm experiments with SSD and YOLOv5, and analyze the experiments to obtain better improvement ideas for small target algorithm.
Multimodal sentiment recognition based on Bi-LSTM and fusion mechanism

The research of multimodal emotion recognition has important application value in artificial intelligence, human-computer interaction and other fields. With the development of deep learning, emotion recognition has been paid more and more attention by researchers. Existing research has solved the problem of unimodal emotion recognition, but neglected the research on the combination of bidirectional long and short neural networks and attention mechanisms. Based on this, we propose an emotion recognition model based on Bi-LSTM and multi-head attention mechanism, which combines the characteristics of LSTM for long-term memory and the advantage that the attention mechanism canquickly screen out more important information among many information, and further improves the accuracy of multimodal emotion recognition.Experimental results show that compared with CNN, CMN, BC-LSTM and other models, this model has better accuracy and f1-score.
Analysis of Factors Influencing the Employment Motivation of Rural Students in Agricultural Vocational Education – The Example of an Agricultural Vocational School in Chifeng

In recent years, Chinese national investment in vocational education has increased, especially in rural areas. However, the large amount of investment has not contributed to the promotion of the quality of vocational education. Moreover, the employment of vocational school students is facing serious challenges. The influence of factors such as backward technology, poor working conditions, and low welfare benefits in rural areas has led to the loss of a large number of talents after they have received vocational education, resulting in a serious lack of agricultural high-tech talents in rural areas. This paper uses research and analysis of questionnaires to study rural students in Chifeng on the issue of rural education, discusses the current employment situation of students in agricultural vocational schools, and analyzes the main motives of rural education values on students’ going to rural areas for employment. The results show that 1) the urban orientation, as well as the utilitarianism of rural education, is not significant; 2) the lack of rural content in rural education affects rural students’ future employment region choice, making rural students more inclined to employment in big cities; 3) the employment difficulty of rural students in the city causes the main influence on rural students’ future employment region choice. At the same time, this paper proposes to change the status quo by combining education with information technology.
The Multiple Subjects and Their Complementary Functions in Educational Governance

Educational governance is an important component of modern education, involving multiple subjects such as the state, government, schools, teachers, students, parents, etc.This article explores the roles and complementary meanings of various subjects in educational governance from the perspective of multiple subjects.Firstly, the roles and roles of each subject in educational governance are analyzed; Secondly, the role of educational governance in industry, academia, and research is explored; Finally, strategies and suggestions are proposed to strengthen cooperation among diverse entities to promote the effective implementation of educational governance.
Research on the Curriculum Reform of Integrated Circuit Majors Based on 1+X

With the rapid development of information technology and the continuous growth of the integrated circuit industry, the curriculum reform of integrated circuit majors in vocational colleges is urgently needed.This study is based on the "1+X" talent cultivation model, aiming to explore how to reform the integrated circuit curriculum in vocational colleges, improve students' comprehensive quality and practical operation ability, and meet the talent needs of the integrated circuit industry.This article first analyzes the implementation and future research directions of curriculum reform for integrated circuit majors in vocational colleges.
ANALYSIS OF STUDENTS’ ABILITY AND DIFFICULTIES IN WRITING DESCRIPTIVE TEXT

This research aimed to identification and analysis students’ ability and difficulties in writing descriptive text. This research used qualitative descriptive methods. The research subject are X grade students of Madrasah Aliyah Negeri 1 Palu. The research took 20 students as sample. This research used interview and test to collect the data. The result of this research shows the students’ ability, the students’ difficulty in writing descriptive text and the factors students’ difficulties in writing descriptive texts. The difficulties are difficulties in generic structure, difficulties in grammar, and difficulties in mechanic, while the factors that cause the students’ difficulties in writing descriptive text are lack of proficiency in the text production skills, and lack of interest in learning English
ANALYZING TEACHERS’ STRATEGIES IN TEACHING VOCABULARY OF JUNIOR HIGH SCHOOLS AT KECAMATAN

The purpose of this research was to investigate the teachers’ strategies in teaching vocabulary to the second grade students of SMP Negeri 1, SMP Negeri 2, and SMP Satap Negeri 1 Kasimbar. The design used was descriptive qualitative. The research subjects were three English teachers teaching vocabulary in three different schools. In collecting the data, the researcher interviewed the three teachers and analyzed the the interviewed results. The results of this research indicate that the strategies used by the three English teachers were different Teacher one used a dialogue strategy, teacher two used showing picture strategy, and teacher three used a question and answer strategy. each of these three strategies has advantages in improving students' vocabulary skills. In addition, the problems faced by the three English teachers in teaching vocabulary to the students in the second grade of junior high schools at Kecamatan Kasimbar also have many factors such as Unstable internet network, lack of student interest in learning English vocabulary, likes to annoy friends in class, most students do not bring a dictionary to school and pronunciation errors. to solve the problems the three teachers Always evaluate the learning given, avoid giving very long assignments, invite students to actively participate, teach note-taking, personal approach, and form groups of students in learning.
APPLICATION OF PARTNER READING STRATEGY IN IMPROVING READING COMPREHENSION

The objective of this research is to find out that the Application of Partner Reading Strategy improves students’ reading comprehension. The population was the grade Eight students at SMP Negeri 1 Toribulu. The researcher used quasi- experimental design. The researcher sample were VIII A as the experimental group which consisted of 28 students and VIII B as the control group which consisted 28 student. The test was administered twice: pre test and post test. The data were analyzed statically in order to find out the significant achievement of the students in the pre test and the post test. The experimental group was given the treatment by applying Partner Reading Strategy, while the control group was taught reading descriptive text with conventional strategy. The instrument used in collecting data was a test given as pre-test and post-test. The result of tests shows that the mean score of experimental group was 88.0 from 52,3 in the pretest, and the control group was 75 from 54,5 in the pretest. This score shows that the mean score of the experimental group is higher than the mean score of the control group on posttest. By applying one-tailed test at 0.05 level of significance with 54 degree of freedom (df), it is found that, the tcounted value of (1,87) is greater than ttable value (1.67). It confirms that, the research hypothesis was accepted. Based on the result, it concludes that, Partner Reading Strategy improves students’ reading comprehension at the eight grade students of SMP Negeri 1 Toribulu.
MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY

The article goes about the methods of training a neural network to recognize fractal structures with the rotation of iteration elements by means of an improved randomized system of iteration functions. Parameters of fractal structures are used to calculate complex parameters of physical phenomena. They are an effective tool in scientific works and used to calculate quantitative indicators in technical tasks. The calculation of these parameters is a very difficult mathematical problem. This is caused by the fact that it is very difficult to describe the mathematical model of the fractal image, it is difficult to determine the parameters of the iterative functions. The neural network learning will allow you to quickly determine the parameters of the first iterations of the fractal based on the finished fractal image and basing on them to determine the parameters of the iterative functions. The improved system of randomized iterative functions (SRIF) will allow to describe the mathematical process and to develop the software for generating fractal structures with the possibility of rotating elements of iterations. In its turn, this will make it possible to form an array of data for training a neural network. The trained neural network will be able to determine the parameters of the figures of the first iterations by means of which it will be possible to build a system of iterative functions. It will help to reproduce a fractal structure qualitatively. This approach can be used for three-dimensional fractal structures. After setting the parameters of the first iterations of the fractal, it will be possible to determine the geometric structure which is the basis of the fractal structure. In the future, this approach may be included in the system for recognizing objects under fractal structures, for example, under masking nets.
Research on the trend and innovation of electronic music production

Nowadays, the level of science and technology in our country gradually develops, and the corresponding electronic music production is experiencing rapid progress.At present, people's demand for the quality of electronic music production is higher and higher.It should not only give people a good sense of hearing, but also be able to combine with high and new technology for application.Throughout the evolution of Chinese electronic music production, it is not difficult to find that it has experienced a process of gradual improvement of technology and is developing towards the direction of computerization.This paper will briefly introduce the evolution process of electronic music production and traditional technologies such as wave table synthesis, addition synthesis, subtraction synthesis, sampling synthesis, modulation synthesis and other sound synthesis methods, and briefly describe the application of the new ROLI Seaboard technology in electronic music production.
New Dimensions of Professional Development of Middle School Foreign Language Teachers from the Perspective of Learning Community

The learning community is the vision for the high-quality development of basic education in the new era.Promoting the construction of a learning community for foreign language teachers in middle school is not only an inevitable requirement for the New English Curriculum reform, but also a realistic need to realize teachers' professional development.The learning community of foreign language teachers in middle school should be committed to creating a home for teachers to have a common sense of belonging and a field for teachers to gain professional dignity and happiness.Learning community should be built by shaping a new teaching and research culture and leading teachers to form a common vision, building a digital learning platform and expanding the space for teachers' professional development, and establishing a developmental evaluation system to promote the sustainable development of teachers.
Research on the Development Path of "Dual-Qualification" College English Teachers under the Background of Industry-Education Integration

The "double-height" construction plan of China's vocational education reform proposes to build high-level vocational schools and high-level majors.The industry-education integration is the key task of the "double-height" construction plan, an important tool for deepening the reform of education, and a key measure to build a high level institution with Chinese characteristics and world standards.The "dual-qualification" teacher team is an important force for the internal development of higher vocational institutions and a fundamental guarantee for improving the quality of talent training.The industryeducation integration is an important carrier for the construction of "dual-qualification" teachers, which realizes the organic integration of industry chain and education chain.This paper analyzes the current situation of English teachers in higher vocational institutions from the background of industry-teaching integration, and finds that there are problems such as solidified teaching concept, single knowledge structure, lack of practical ability and imperfect teacher development mechanism of college English teachers.In view of these problems, the development path of " "dual-qualification " college English teachers is proposed: College English teachers need to update their education and teaching concepts, improve their comprehensive quality, and construct a perfect development mechanism for "dualqualification " college English teachers.We hope to promote the development of college English teachers, build an excellent "dual-qualification" college English teachers' team, and help the construction of "double-height" construction plan for higher vocational education.
Outline of the implementation and research of the management policy of the education leadership department from the perspective of global new media

At present, a new media era has been formed in the global scope, and the management work of education leaders is also facing opportunities and challenges under the new situation.Based on the current global situation, to explore the development strategy close to the current and contemporary trend, analyze the new path of the current education management from the perspective of global development, infiltrate the advantages of new media into the education management, and explore the education management implementation strategy that is more suitable for the development of The Times and can stimulate the efficiency of education management.
Research on Performance Appraisal Method of Private Colleges and Universities Based on Personnel Incentive

With the development of society, people's requirements for education are constantly improving, so private colleges and universities have also achieved rapid development, playing an important role in China's higher education.However, compared to public universities, public universities implement a unified national enrollment and fee collection system, while private universities implement independent enrollment and fee collection systems, making talent selection somewhat difficult.Therefore, reasonable improvement and establishment of scientific and effective performance evaluation methods have become the key to improving teaching quality and enhancing the enthusiasm of teachers.This article will combine the current situation and existing problems of performance appraisal in private colleges and universities in China, and propose a performance appraisal method based on personnel motivation, which can effectively improve the teaching quality and employee enthusiasm of private colleges and universities.
A Holistic Study on Aspect-Based Sentiment Analysis

Sentiment analysis, often known as opinion mining, is a technique used in natural language processing to determine the emotional undertone of a document. This is a common method used by organizations to identify and group ideas regarding a certain good, service, or concept. In business sectors, sentiment analysis is crucial in decision-making. Aspect-based sentiment analysis is a type of sentiment analysis that helps in company’s overall improvement by letting than know which characteristics of their products they should enhance in response to client’s feedback in order to turn them into top sellers. Many researchers in this area applied machine learning and deep learning approaches. There are issues regarding performance and accuracy in conventional machine learning sentiment analysis approaches. Thus, there is a need to improve accuracy and performance during aspect-based sentiment analysis. The objective of this research is to present a detailed review and comparative analysis of machine learning and deep learning approaches in aspect-based sentiment analysis.
Automatic Machine Translation System with Artificial Intelligence Theory

At present, with the continuous development of computer technology, automatic machine translation system has been adopted by many universities and research institutes, and it has entered the stage of practical and commercialization.In recent years, with the continuous development of artificial intelligence technology, a lot of research has been conducted on it.Based on this current situation, this paper conducts a research and analysis on the automatic machine translation system of artificial intelligence theory.
Chinese Named Entity Recognition Based on Multi-Feature Fusion FLAT Model in the Medical Field

The complexity of syntax and the specialized nature of Chinese electronic medical record data make it challenging to accurately identify medical entities using named entity recognition models.In order to precisely extract complex medical vocabulary from electronic medical records, this paper proposes a multi-feature-based named entity recognition model that addresses the issue of insufficient internal feature extraction in the FLAT model.Firstly, the radical and pinyin features of Chinese characters are extracted for enrichment and perfection of their semantic information.These features are then combined with the word embeddings extracted by the FLAT-lattice method.Finally, the fused features and position encoding are input to the Transformer encoder for encoding, followed by decoding using the CRF method.Experimental results demonstrate that the proposed model outperforms many existing algorithms on the CCL2021 dataset, with an F1 score of up to 91.75%.
An Algorithm and Architecture Co-design for Accelerating Smart Contracts in Blockchain

Modern blockchains supporting smart contracts implement a new form of state machine replication with a trusted and decentralized paradigm. However, inefficient smart contract transaction execution severely limits system throughput and hinders the further application of blockchain.
Impact of Remote Work on Software Teams: A Qualitative Study

The COVID-19 pandemic introduced a new scenario to several software companies, which were forced to adopt home office work. The new situation imposed by the COVID-19 pandemic presents several challenges for companies, particularly in how they manage development teams and the knowledge produced. This work focuses on understanding the main modifications in software development teams when changing their work mechanisms from face-to-face to remote. A study through interviews was performed and qualitative data analysis was carried out. The results show that remote work brought both advantages and disadvantages compared to face-to-face work. Furthermore, companies with well-defined processes had less difficulty adapting to remote work.
DR.QG: Enhancing Closed-Domain Question Answering via Retrieving Documents for Question Generation

In this paper, we propose a new framework called Document-Retrieval Question Generation (DR.QG). The goal of DR.QG is to generate a question corresponding to a given answer. Differing from the common question generation setting, DR.QG takes only answers for question generation, while existing question generation takes a context passage and an answer as input for generating. To achieve this goal, we explored the possibility of importing document retrieval. Through the performance evaluation on the Question Answering (QA) task, we demonstrate the feasibility of DR.QG. The result shows that our method improves QA performance by up to 13%. Furthermore, we simulate the closed-domain situation on the open-domain dataset and show that we improved performance by 3%.