Reviewerly/exHarmony huggingface dataset:
Research on Text Sentiment Analysis of Movie Reviews Based on BERT Model

With the rapid development of machine learning technology, more and more industries pay attention to the research and application of data science. In recent years, many industries such as e-commerce, service, movie and television, news and so on have begun to attach great importance to and deeply analyze the sentimental tendency of user comments in the network. Sentiment analysis is an important branch of NLP. This paper takes the movie reviews dataset published by Stanford as the research object, and uses the bert-base-uncased pre-training model in BERT to study the text sentiment classification of the dataset. In the fine tune phase of the model, Adam and CrossEntropyloss are selected as the optimizer and loss function, and at the same time, the influence of parameters learning_rate and batch_size on the accuracy of text classification is studied. The experimental results show that learning_rate and batch_size has a certain impact on classification accuracy. When learning_rate=2e-5, the classification accuracy of the model on this dataset can reach up to 87%, achieving a relatively ideal classification effect.
CopyFormer: A Transformer Text Generation model combined with Pointer Network

Industrial Internet Research Institute Wuhan Huazhong Numerical Control Co., Ltd.Wuhan, 430074, China
Implementation of Sequence Model For Learning Phenotype Structure

Little DNA patches bonded to a solid surface make up a microarray.In microarray studies, a gene's expression level is estimated using the signal gathered from each location.There are hundreds of DNA locations on a microarray.Finding phenotypic structures is a key issue in microarray data analysis.The goals are to 1) identify groupings of samples that represent various phenotypes (such as illness or normal phenotypes), and 2) identify the usual expression pattern for each sample assemblage.The overall disadvantage is that identified signatures frequently contain many genes yet have weak discriminative abilities.The ordered appearance values among genes are productively used in this proposed model, which is a g*sequence model to report this constraint.An algorithm called FINDER is built in this procedure.
A Novel Image Encryption Algorithm Based on Cellular Automata and Chaotic System

This paper presents an image encryption algorithm based on cellular automata and chaotic systems. The basic unit of cellular automata is a single cell. Cellular automata evolve based on the rules of evolution by combining the states of cells and their neighbors. Cellular automata can evolve complex behavior states. Using the random sequence of the hyperchaotic system, the random sequence is mapped to the cell value, and the cell is randomly replaced in the process of image scrambling. This method greatly improves the parallelism of the algorithm. Through the analysis of the experimental results and security of the encryption algorithm, it is found that the method has a sufficient anti-attack ability. Therefore, it is very suitable for application in the actual system.
Optimization Scheme of PBFT Consensus algorithm based on dynamic grouping of credit ratings

The PBFT consensus algorithm was proposed in 1999 to solve the problem of inefficiency of the original Byzantine fault-tolerant algorithm and reduce the complexity of the algorithm from exponential to polynomial level, making the Byzantine fault-tolerant algorithm feasible in practical system applications. However, the higher-side consensus complexity still has a large impact on the overall performance of the system. In this paper, we propose an improved PBFT consensus algorithm based on a dynamic grouping by credential level, the Level Group Byzantine Fault Tolerance (LGBFT) consensus algorithm. The improvement is aimed at the problem of low performance of the Byzantine fault-tolerant algorithm which has been used in the current blockchain when the number of nodes is large. A simulation system was built using the Golang language for performance testing. The experimental results show that LGBFT has higher performance than PBFT after multiple consensuses, and has greater performance improvement with a larger number of malicious nodes.
A Cost-Effective Online Resource Scheduling using Concave Pricing for Cloud Computing

Cloud computing is the term used to describe a paradigm for providing universal network access to a shared set of reconfigurable computer resources.Users and businesses may utilise solutions for cloud computing and storage that let users process and store their data at other people's facilities centres in a variety of ways.Similar to a utility through a network, it depends on resource sharing to achieve coherence and economies of scale.The fundamental concept behind cloud computing is converged infrastructure and shared services.Most Infrastructure-as-a-Service (IaaS) cloud service providers provide a variety of price plans to appeal to clients with different needs.Maintaining one's competitiveness among other cloud service providers is crucial from the standpoint of the specific cloud service provider.A cloud broker can maximise the savings provided by cloud service providers by intelligently allocating resources to various clients.This study focuses on the cost-effective online resource scheduling that a broker might use to assist a group of clients in utilising the volume discount pricing technique offered by cloud service providers.This project's technology offers a space consumption algorithm to cut down on costs and attract more users.
Privacy-Preserving and Approximately Truthful Local Electricity Markets: A Differentially Private VCG Mechanism

Privacy-aware market participants care about the leakage of their private information via statistical releases of local electricity markets outputs. This kind of privacy breach would have major implications on the future transactions of the market participants and unauthorized observers’ beliefs about them. To address this challenge, we introduce the notion of noisy electricity markets in the framework of Differential Privacy (DP) for preserving the privacy of individuals and maintaining the utility of their data for social good. In this regard, this paper proposes a novel differentially private mechanism for local electricity markets that releases a near-optimal solution while guarantying the outputs of the market would reveal almost nothing about any individual’s input data. To do so, we implement the exponential mechanism for privatizing the baseline Vickrey–Clarke–Groves (VCG) mechanism in the proposed local electricity market. Moreover, we provide an upper-bound on the social welfare loss incurred by the privacy constraint and analyze the inherent trade-off between the privacy and suboptimality. In the end, numerical case studies for reflecting the theoretical properties of the proposed mechanism are provided.
Federated Learning for Robust Computer Vision in Intelligent Transportation Systems

We study Federated Learning (FL) as the promising technology to design the computer vision Machine Learning-based applications, which are robust to possible Data Quality (DQ) degradation in real Intelligent Transportation Systems (ITSs). As ITSs are composed of diverse data generation and communication units, DQ of images they produce and use may vary dramatically. We study the images of various quality distributed over the local clients and perform iterative FL training with two distinct aggregation strategies: Federated Averaging (FedAvg) and Geometric Median (GM). We consequently evaluate the models cross-trained over a single DQ cohort against other DQ categories. Then we analyze the image (traffic signs) recognition performance and its robustness toward the DQ degradation for each training cohort and aggregation strategy. Based on the results, we provide our recommendations on how to train more robust FL-based computer vision applications for ITSs.
A Fine-grained Detector of Face Mask Wearing Status Based on Improved YOLOX

The fast outbreak of COVID-19 and rapid prolif-eration of its variants have continued to pose a huge challenge to people around the world. Wearing medical masks properly in public and private settings can protect people from COVID-19 which brings a growing demand for automatic detection services of face mask wearing conditions. In this paper, we propose a fine-grained detector called ECA YOLOX-S to identify the wearing status of face masks. Efficient Channel Attention (ECA) is introduced into YOLOX-S to reach a trade-off between effectiveness and efficiency. To demonstrate the performance of our proposed method, a Fine-grained Face Mask (FineFM) dataset is created which covers four classes of mask wearing status. The proposed FineFM dataset has 16,955 annotated images and covers multiple realistic scenarios, which is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HongliXiao/FineFM</uri> . To our knowledge, it has the largest number of improper mask wearing images among all similar datasets for realistic scenes. Experiments conducted on the FineFM dataset demonstrate that ECA YOLOX-S achieves an overall mAP@.50:95 of 86.80% for moderate scenes and an overall mAP@.50:95 of 73.20% for complex scenes, outper-forming its benchmark model. Moreover, experiments conducted on other realistic and simulated datasets indicate the proposed detector has advantages over other methods.
The Impact of Synthetic Data on Membership Inference Attacks

Privacy of machine learning on Big Data has become a prominent issue in recent years due to the increased availability and usage of sensitive personal data to train the models. Membership inference attacks are one such issue that has been identified as a major privacy threat against machine learning models. Several techniques including applying differential privacy have been advocated to mitigate the effectiveness of inference attacks, however, they come at a cost of reduced utility/accuracy. Synthetic data is one approach that has been widely studied as a tool for privacy preservation recently but not much yet in the context of membership inference attacks. In this work, we aim to deepen the understanding of the impact of synthetic data on membership inference attacks. We compare models trained on original versus synthetic data, evaluate different synthetic data generation methods, and study the effect of overfitting in terms of membership inference attacks. Our investigation reveals that training on synthetic data can significantly reduce the effectiveness of membership inference attacks compared to models trained directly on the original data. This also holds for highly overfitted models that have been shown to increase the success rate of membership inference attacks. We also find that different synthetic data generation methods do not differ much in terms of membership inference attack accuracy but they do differ in terms of utility (i.e., observed based on train/test accuracy). Since synthetic data shows promising results for binary classification-based membership inference attacks on classification models explored in this work, exploring the impact on other attack types, models, and attribute inference attacks can be of worth.
Poisson Image Editing

Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection.
Research on Hotspots and Trends of Chemistry Teaching Informatization in China over the Past Ten Years: Visual Analysis Based on CiteSpace

With the implementation of the Action Plan of Education Informatization 2.0, the process of education informatization has achieved great success, and digital technology has had a significant impact on education management and participants at all levels.This paper uses the Chinese Journal Full-text Database (CJFD) as the data source to conduct statistics, and uses CiteSpace to statistically analyze the research on chemistry teaching informatization in China from 2013 to 2022.Based on the results of visual analysis, relevant research hotspots in China were summarized, which mainly focus on three frontier areas: digital experimentation with hand-held technology, quadruple representation teaching model, and teaching strategies and applications in the context of informatization.This paper will provide suggestions and references for Chinese teachers and educational researchers.
Characterization of quantumness of non-Gaussian states under the influence of Gaussian channel

The impact of a noisy Gaussian channel on a wide range of non-Gaussian input states is studied in this work. The nonclassical nature of the states, both input and output, is developed by studying the corresponding photon statistics and quasi-probability distributions. It is found that photon addition has more robust quantum mechanical properties as compared to the photon subtraction case. The threshold value of the noise parameter corresponding to the transition from partial negative (W and P) and zero (Q) to completely positive definite, at the center of phase space, depends not only on the average number of thermal photons in the state but also on the squeezing parameter. In addition, it is observed that the nonclassicality of the kth number filtrated thermal state could be further enhanced by adding photon(s).
Agile meeting method for building intelligent decision support systems

In this article, we present an agile method based on a cycle of meetings that guides the construction of intelligent decision support systems. This method presents the phases of initiation, analysis and planning, negotiation, control and intelligent decision support. A cycle represents a passage through all the phases of the method, where as the execution of a phase means that all the planned meetings were held. Each meeting lasted 15 min, and input and output were composed of artifacts that supported the evolution of each meeting. In the initial phase, a meeting was held with everyone with the cards for the survey of the requirements and the construction of the 3D graph to represent the size. In IT meetings, artifacts, forms and tables were used to define the first packages. In the analysis and planning phases, the objectives by key results form were used. In the negotiation, we use the structural sets form. In the control phase, we have the configuration artifact and its control graph. Finally, in intelligent decision support, we use the essential questions form. The method serves as a guide for building intelligent decision support systems that can help with problems like determining whether or not to sign a contract.•In the initial phase, cards for requirement gathering together with a complexity graph and Board Requirements by Layers and Key Person supported the organization of development packages.•In the control phase, the input structures enabled the creation of a continuous control artifact. Furthermore, the control chart showed what is in scope and is part of ongoing control.•The intelligent decision support phase guaranteed the refinement of requirements, which brought intelligence criteria to the development packages and gave them their unique characteristics.
Development and Application of Ash Fusion Temperature (Deformation Temperature) Prediction Model for Woody Biomass

Ash agglomerates deposited on the heat exchanger causes slagging and fouling, which decreases efficiency of heat exchanger of boilers. The ash fusion temperature can be important factor of slagging and fouling. This paper presents a prediction model for deformation temperature of woody biomass ash. There are two prediction model: one is quadratic and cubic model using index A by correlation coefficient, and other model is calculated by multiple linear regression using components and indices of previous studies. The coefficients of determination(R2) of quadratic model, cubic model, and multiple linear regression model are 0.835, 0.8367, 0.9175, respectively. Therefore, multiple linear regression model can predict deformation temperature of biomass ash.
Exploring the direction of developing achievement standards for competency-based curriculum through analysis of previous studies related to content standards and performance standards

Objectives The purpose of this paper is to solve the confusion about the statement of achievement standards in Korea in relation to the meaning of content standards and performance standards, and to explore the direction of development of achievement standards for competency-based curriculum. Methods To this end, the content of 12 previous papers dealing with the meaning of content standards and performance standards were analyzed. Results As a result of the analysis of previous studies, the meaning of content standards is divided into two categories; ‘standards for what students need to know’ and ‘standards for what students need to know and can do’. Performance standards are ‘standards of what can be done’ if content standards are defined in the former meaning, and if defined in the latter meaning, it turns out that the performance standard can be understood in the following three meanings; a statement focusing on the application of knowledge, a statement focusing on the application of knowledge with a clue for achievement, criteria for evaluating the level of achievement or mastery of how well the knowledge and skills included in the content standard are learned as a result. Conclusions As a result of searching for the cause and solution of the confusion about the meaning of content standards and performance standards, content standards are ‘standards of what to know and can do’, and performance standards are criteria for evaluating the level of achievement or mastery of how well the knowledge and skills included in the content standard are learned as a result. As a direction for development of achievement standards for competency-based curriculum, a method of presenting achievement standards by each category of knowledge, skills, values and attitudes, rather than being stated as a combination of content elements of knowledge, skills, values and attitudes, was proposed.
An Exploratory Study on Influencing Factors on the Desire of Learning AI_focusing on the 5,6th grade in elementary school

Objectives The aim of this study was to investigate elementary students’ experience with AI, their recognition, the desire of learning AI technology assumed innovation-technology from the perspective of AI-philia, and to analyze variables how much independent variables influence on the desire of learning AI technology. Methods 245 fifth and sixth graders in elementary schools in metropolitan city were respondents, who answered based on pencil and paper survey. M(Mean) and SD(Standard Deviation) of variables(experience with AI technology, perceived usefulness, innovation tendency, needs on educating parents to be knowledgeable, how much complicated AI society , and the desire of learning AI), T-test for statistical significance between male and female, ANOVA for among 4-different experienced groups, Stepwise Multiple Regression Analysis for how much independent variables influence on the desire of learning AI were conducted. Results The result was that Bystander(inexperienced-AI group) displayed the lowest point of all variables, reversely as expected the innovator(the highest experienced-AI group) showed the highest mean. Gender difference in perceived ruefulness and the desire of learning AI, also statistically significant from the two groups like between Bystander and Innovator, between Taster and Innovator in Innovation tendency were revealed. The power of impacts on the desire of learning AI showed orderly innovation, gender, needs on educating parents, and perceived usefulness. Exceptionally a variable of AI experience was excluded from regression analysis. Conclusions This study is meaningful to capture AI natives's experience and how they perceive AI technology and will give a basic cue for the method of teaching and learning in AI education praxis in Elementary school. Additionally the further research is proposed to compare between leading schools and ordinary schools of AI literacy education and add various variables.
A Study on the Importance of Learner ‘Local Experience’ in Local University Innovation

Objectives This study critically points out that the existing discussions on regional universities and regional innovations have overlooked the ultimate subject of innovation, ‘local learners’ and their ‘local experiences’, and points out that the current ongoing local university innovation It was done to argue that the learner's ‘local experience’ should be taken into account in the discussion. Paying attention to the nature of the local university innovation project as an educational plan, this study attempted a theoretical review of the ‘local experience’ of local learners, a fundamental problem that had been overlooked in previous discussions, and suggested future directions. Methods To this end, this study critically examines recent environmental changes and issues surrounding local universities, and attempts a conceptual examination of the ‘local experience’ of learners at local universities, which is a fundamental problem that has been overlooked in existing policy approaches to local universities. In addition, based on this series of discussions, by presenting domestic and foreign cases that are achieving university innovation by considering the ‘local experience’ of learners as a relatively exemplary model, focusing on the ‘local experience’ of learners, local learners, local universities, and the community It was intended to suggest a policy direction for practical contribution to development. Results Based on the discussion, this study proposes directions for regional university innovation based on the learners' local experiences. The proposed directions include: 1) Suggestions for enabling local learners to settle in the region and meaningfully utilize the tangible and intangible assets of the region. 2) Suggestions for utilizing the holistic experience of society, economy, and culture in the formation of mutually beneficial relationships among various stakeholders. 3) Suggestions for transforming learners' local experiences into practical needs within the local community. Conclusions In order for policies related to local university innovation to be implemented in an educationally desirable direction in a win-win structure with various institutions, it is essential to consider the ‘local experience’ of local university learners. This study is meaningful in that it tries to raise a new problem from an educational point of view for the regional university innovation policy, which has been carried out from an existing administrative and political point of view.