Reviewerly/exHarmony huggingface dataset:
Integrating Grammarly Tools to Enhance Writing Efficiency in Senior High School

The background of this research is that the writer wants to examine the use of grammar in improving writing efficacy. The purpose of this study was to find out the application of Grammarly to improve the efficiency of writing skills in senior high schools. Researchers used descriptive qualitative methods by using interviews, observations, and questionnaires. In this study, 30 participants were observed, and at the end of the interview, eight students with high questionnaire results were taken to be interpreted. Participants are high school students studying in the 2022/2023 school year. The results and discussion show that Grammarly helps high school students learn writing. Students are interested and confident in using Grammarly with the various advantages available during the learning process. This tool is also practical to use and easy to access, so it doesn't make it difficult for students. Students showed positive views of using Grammarly and felt that it was more helpful in exploring writing skills using Grammarly with their various advantages.
A Study on the Teaching of Chinese Verb Classifier Based on Corpus and Textbook Analysis

Chinese verb classifier is not only varied in types but also complicated in meaning, making it difficult for foreign learners to fully understand and utilize them. In addition, current Chinese classifier education, including textbooks, focuses on the teaching of noun classifier, which limits the efficiency of learning verb classifiers. Furthermore, compared to the teaching of noun classifier, there are fewer studies on the teaching of verb classifiers, which limits the ability to conduct more systematic teaching.BR Therefore, this paper examined the current status of verb classifier education through corpus and textbook analysis for effective verb classifier education in the future. First, we collected example sentences of Korean learners
A Group Anonymity Based Federated Learning Framework

As federated learning gradually enters the application field, its data privacy guarantee has received more and more research attention. Although federated learning can avoid direct exchange of personal data through gradient aggregation, many studies have shown that attackers can obtain user privacy information only through gradients. In addition, the security of the model itself is also threatened. Another type of attack on federated learning is to pretend to be a user and control the final model by carefully constructing the submitted data. However, current defenses against these two attacks are contradictory. The former needs to shield participants from directly obtaining gradients submitted by users, while the latter needs to use these data to analyze potential malicious users. In order to balance the privacy and security of federated learning, we construct an anonymity method, group anonymity, a privacy guarantee that everyone cannot distinguish submitters from each other, to protect user privacy while allowing the server to detect malicious submissions. Our implementation is based on ring signature and blockchain. Ring signature realizes anonymous authentication of users. The blockchain provides a trusted collaboration environment, which prevents deception of individual users and avoids fishing on users. Experiments demonstrate the effectiveness of our framework against label flipping attacks and reconstruction attacks.
PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.
Will LLMs Reshape, Supercharge, or Kill Data Science? (VLDB 2023 Panel)

Large language models (LLMs) have recently taken the world by storm, promising potentially game changing opportunities in multiple fields. Naturally, there is significant promise in applying LLMs to the management of structured data, or more generally, to the processes involved in data science. At the very least, LLMs have the potential to provide substantial advancements in long-standing challenges that our community has been tackling for decades. On the other hand, they may introduce completely new capabilities that we have only dreamed of thus far. This panel will bring together a few leading experts who have been thinking about these opportunities from various perspectives and fielding them in research prototypes and even in commercial applications.
Monitor and Troubleshoot an Azure Cosmos DB Solution

Proper monitoring is critical for any application and helps you understand the health and utilization of your database service. Like most data services, Azure Cosmos DB has a control plane and a data plane that manages and executes activity against the service. The control plane includes activities used to control your service such as adding new databases and containers or setting throughput. The data plane includes any activity related to the data stored in the account such as reads, writes, queries, and deletions.
Improving Students' Writing Skills through Group Work

This research was aimed to improve students' writing skills through group work. The subject of the research were the 11th IPA 7 of SMA Negeri 1 Natar that consisted of 36 students. This research used Classroom Action Research (CAR) which was consisted of two cycles. Collection data technique was used test and non-test. Writing test used to know students’ writing skill. Menwhile, observation sheet and interview for non-test. Data analyzing was used quantitative and qualitative method. Refers to the result of the test, the mean score of students’ learning activities cycle 1 was 6.65 with the criteria was less active. Meanwhile in cycle 2 was 7.57 with the criteria was active. The improvement was 0.92. In the other hand, mean score of writing test in cycle 1 was 60 or 8 students who got score ≥70 with the percentage 22%. Meanwhile, mean of writing test in cycle 2 was 75.57 or 20 students who got score ≥70 with the percentage 55%. The improvement was 33% or 15.57. Based on the result of the research, Group Work can improve students’ writing skill in the 11th IPA 7 of SMA Negeri 1 Natar.
THE USE OF THE QUANTUM TEACHING MODEL ON STUDENT LEARNING OUTCOMES IN MATHEMATICS CLASS II SDN 158/VIII RIMBO MULYO

The use of learning models that are not appropriate during the learning process causes students to get bored and bored so that students are less active when learning activities take place so that students do not understand the material because there is too much material and most of them take notes, and the impact on learning outcomes is very low. The Quantum Teaching Model This researcher aims to improve learning outcomes with the Quantum Teaching model for Class II SDN 158/VIII Rimbo Mulyo. This type of research is class action research (Classroom Action Research). This study aims to determine learning in mathematics subject matter of time measurement. The research subjects were class II students at SDN 158/VIII Rimbo Mulyo. Methods of data collection is done through documentation, observation and tests. The work procedure in this study is an activity cycle consisting of two cycles. Where each - each cycle through the stages - the stages that must be taken namely planning, implementation of action, observation, reflection. The completeness criterion is 70 where the learning outcomes of students are said to be complete if they meet these criteria. The results of the research show that the Quantum Teaching model can improve student learning outcomes. This can be seen from the average pre-cycle score of 61.21, the first cycle is 67.88 and the second cycle is 73.03. While the percentage in the pre-cycle was 42.42%, the first cycle was 60.61% and the second cycle was 81.81%. This means that research at this stage is sufficient because students have met the specified KKM, namely 70.
A method of speech information hiding in inactive frame based on pitch modulation

To solve the problem that the speech information hiding algorithm based on random position selection and matrix coding has insufficient hiding capacity, the paper proposes a novel pitch modulation steganography method based on inactive frame. In this method, the least significant bit (LSB) replacement method is adopted for inactive frames, and the speech information hiding algorithm based on random position selection and matrix coding is adopted for active frames, which realises the pitch modulation information hiding method based on inactive frames. Finally, simulation experiments are carried out for the pitch modulation information hiding method based on inactive frames. The results indicate that the maximum hiding capacity of the algorithm in this paper can reach 241.67 bps, which significantly improves the hiding capacity, and at the same time the concealment has also been improved to a certain extent.
An Exploration of Students’ Current Reading Habits and Skills

This study is an exploration of students' reading habits and skills. This research employed a cross-sectional survey design to assess students' reading habits and skills. In a cross-sectional study, data is gathered in general to concentrate on a populace at a single point to look at the connection between variables of interest. Some inconsistencies in data trends were found in reading frequency and duration. This may indicate that higher reading frequencies and/or longer reading durations does not necessarily translate to better reading comprehension. Frequent reading of a material that does not effectively reinforce reading comprehension skills may just be as effective as infrequent reading of a material of substance. The findings of the study will be helpful for Academic Institutions, Educators, and Students. In the first place, this review will assist Academic Institutions with working on the school in the advancement of reading programs for student improvement. This study will cultivate better approaches for improving learning, skills, and abilities, thus training universally driven students in the future.
Explainability through uncertainty: Trustworthy decision-making with neural networks

Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under distribution shifts, where the model performance silently degrades as the data distribution diverges from the training data distribution. Uncertainty estimation offers a solution to overconfident models, communicating when the output should (not) be trusted. Although methods for uncertainty estimation have been developed, they have not been explicitly linked to the field of explainable artificial intelligence (XAI). Furthermore, literature in operations research ignores the actionability component of uncertainty estimation and does not consider distribution shifts. This work proposes a general uncertainty framework, with contributions being threefold: (i) uncertainty estimation in ML models is positioned as an XAI technique, giving local and model-specific explanations; (ii) classification with rejection is used to reduce misclassifications by bringing a human expert in the loop for uncertain observations; (iii) the framework is applied to a case study on neural networks in educational data mining subject to distribution shifts. Uncertainty as XAI improves the model’s trustworthiness in downstream decision-making tasks, giving rise to more actionable and robust machine learning systems in operations research.
Feature recognition of English clauses based on particle swarm optimization algorithm

Feature recognition of English clauses is a basic problem of syntactic analysis. It is the basis of English-Chinese machine translation. A feature recognition method of English clauses based on particle swarm optimization algorithm is proposed. This paper analyzes the characteristics of English clauses, delimits the boundary of clauses, and follows the current optimal particle in the solution space to search the best position through the cooperation and information sharing between particle swarm individuals. The feature set is selected, the crossover and mutation idea of genetic algorithm is introduced, and the crossover operation is carried out to complete the feature recognition of English clauses. The experimental results show that when the threshold P is 50, the recognition accuracy of this algorithm is consistent with that when p is 100, and the recognition accuracy is 93.45%. The accuracy of particle swarm optimization algorithm for English clause feature recognition is high, which remains at about 90%. Compared with the two literature methods, the convergence performance of particle swarm optimization algorithm is better.
Dynamic multi-objective workflow scheduling for combined resources in cloud

Cloud resource providers offer idle resources to users as spot instances. The price of the instances changes with market supply and demand, and the dynamic price can have a significant impact on workflow scheduling. In this work, we use a combination of spot and on-demand instances as the foundation cloud resource and characterize the dynamic workflow scheduling problem as a dynamic multi-objective optimization problem (DMOP), where the dynamics originate from the dynamic price of spot instances. The scheduling solution is found by considering three objectives: maximizing the reliability of the instances while minimizing the makespan and cost. In addition, we provide an enhanced MOEAD algorithm called MOEA/D-URDI that combines diversity introduction and uniform random sampling, where the uniform random sampling paradigm is used to generate the initial weight vector. The dynamic multi-objective optimization evolutionary algorithm DMOEA/D-URDI is then created by combining the method with a dynamic optimization framework. Our technique beats existing algorithms, according to experimental data based on dynamic benchmark sets and three well-known scientific procedures in terms of metrics on dynamic benchmark sets and better ensures reliability in scheduling scientific workflows while reducing makespan and cost.
Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks

Social bot detection is a challenging task and receives extensive attention in social security. Previous researches for this task often assume the labeled samples are abundant, which neglects the fact that labels of social bots are usually hard to derive from the real world. Meanwhile, graph neural networks (GNNs) have recently been applied to bot detection. Whereas most GNNs are based on the homophily assumption, where nodes of the same type are more likely to connect to each other. So methods relying on these two assumptions will degrade while encountering graphs with heterophily or lack of labeled data. To solve these challenges above, we analyze human-bot networks and propose SIRAN, which combines relation attention with initial residual connection to reduce and prevent the noise aggregated from neighbors to improve the capability of distinguishing different kinds of nodes on social graphs with heterophily. Then we use a consistency loss to boost the detection performance of the model for limited annotated data. Extensive experiments on two publicly available and independent social bot detection datasets illustrate SIRAN achieves state-of-the-art performance. Finally, further studies demonstrate the effectiveness of our model as well. We have deployed SIRAN online: https://botdetection.aminer.cn/robotmain .
Pengaruh Metode Pembelajaran Picture and Picture terhadap Motivasi dan Pemahaman Konsep Siswa

There are still many teachers who use a monotonous method when teaching. The use of monotonous methods causes students to be less active, become bored and not serious in following lessons, so that students' motivation and learning outcomes are low. This study aims to determine the effect of the picture and picture learning method on the motivation and understanding of the concepts of seventh grade students of SMP Negeri 1 Sakra in the 2018/2019 academic year. This type of research is quasi-experimental. The population in this study were seventh grade students of SMP Negeri 1 Sakra. Samples were taken using a saturated sampling technique, namely class VII B as the experimental class and VII A as the control class. The results showed that at the first meeting in the control class, the percentage of students' learning motivation was 70.69%, while at the second meeting the percentage was 73.05% with the same category of high motivation. The results of the questionnaire on learning motivation in the first meeting in the experimental class were 76.38%, at the second meeting the percentage was 76.33% including the same category of high motivation. Concept understanding data is seen from the acquisition of pre-test and post-test scores. The average value of the control class students at the time of the pre-test was 27.88 the percentage of 5% with the category of a small percentage of students understanding the concept. Then the average score of students at the time of the post-test was 48.14 with a percentage of 22% in the category of a small percentage of students understanding the concept. The average value of the experimental class students at the time of the pre-test was 29.25, the percentage of 11% with the category of a small percentage of students understanding the concept. Then the average score of students at the time of the post-test was 63, a percentage of 78% with the category of almost all students understanding the concept. The data on psychomotor learning outcomes for the control class at the first meeting was 68.88%, at the second meeting 63.33% in the quite skilled category. Then the experimental class at the first meeting was 79.16% at the second meeting at 78.47% with the same category, namely skilled. The post-test data were then analyzed using t-test, obtained tcount &gt; ttable, or 112.4 &gt; 1.98, so it can be concluded that there is an effect of picture and picture learning method on students' motivation and understanding of concepts.
The Practical Difficulties and Implementation Paths of Career Development Planning for College Students in the New Era

Employment is the most basic livelihood. In the new era, good career planning for college students can help them better position themselves, set career goals, and improve the quality of talent cultivation. lt is also an inherent requirement for universities to implement the fundamental task of "cultivating morality and cultivating talents". However, with the transformation of higher education in China from elitism to popularization, colleges and universities continue to expand enrollment, the number of college graduates is increasing year by year, and the employment situation is severe, which has brought heavy pressure to college students' employment. At present, when universities make career planning for college students, they face challenges such as weak planning awareness, inaccurate self-positioning, and biased value orientation. Based on the above problems, we should strengthen college students' awareness of career planning, correctly set up career goals, cultivate correct value orientation in social practice, and promote college students to make good career development planning.
Research on Influencing Factors of Scientific Research Performance Evaluation Satisfaction Degree of Humanities and Social Sciences Teachers in Universities

In order to find out the factors affecting the satisfaction degree of scientific research performance evaluation of humanities and social sciences teachers in universities, the study conducted empirical research. Aiming at the influence of individual characteristics and individual status on the satisfaction degree of scientific research performance evaluation of humanities and social sciences teachers in universities, the study puts forward 8 research hypotheses. Through the linear regression test, it is found that age, educational background, individual research autonomy degree, individual research performance and individual salary are the positive factors affecting the satisfaction degree of scientific research performance evaluation of humanities and social sciences teachers in universities. The administrative position is a negative factor affecting the satisfaction degree of scientific research performance evaluation of humanities and social sciences teachers in universities.
Exploration of Digital Innovation Path in Dance Curriculum

Digital course teaching has ushered in a new trend in school dance education, facilitating the seamless integration of digital technology and dance education while maximizing access to high-quality online and offline resources. This paper analyzes innovative ideas and practical approaches for developing digital dance curricula that align with current trends in China's digital education landscape. The analysis reveals that deep integration between information technology and dance education is the way forward. To create effective digital dance curricula, we must enhance the use of digital tools in teaching while cultivating a team of highly skilled, innovative grassroots teachers. Additionally, we should prioritize replicable teaching achievements that can be scaled up to support sustainable development of digital dance curriculum resources and contribute to ongoing educational reform efforts.
The application and development of reinforced concrete structure in building construction

With the acceleration of the current social development, more and more people's demand for life is also gradually improving. The quality of housing construction and environment also has higher requirements. The reinforced concrete structure is one of the main application structures in the current project. Its good performance is the basic support to strengthen the performance of housing construction, but there are also some problems. At the same time, due to the rapid development of the current construction industry, a large number of new materials and new technologies began to be applied to the market. Based on this, this paper analyzes the application of reinforced concrete structure technology in housing construction, makes full use of its advantages and avoids its disadvantages as far as possible, and finally puts forward the future development strategy of the structure in housing construction.