Reviewerly/exHarmony huggingface dataset:
Cooperative Blended Learning Model Applied to the Next Generation Digital Learning Environment

The paper designs a cooperative blended learning model applied to the next generation digital learning environment which called Online Learning Platform (OLP 2.0). In OLP 2.0, teachers cooperate with each other to decompose relevant knowledge information, produce meta-data information, collect teaching cases and form game maps with the support of three courses which are Data structure, Algorithm design, and Program design course group; learners are guided into the learning situation in which the knowledge sequences are set up referring to the implementation process of teaching cases; learners can learn knowledge and gain abilities with the help of game maps. Three types of representative game maps which are linear maps, tree maps, and graph maps are designed to meet the demands of learning knowledge and cultivating cooperation abilities. Each map needs to be completed by at least two learners. And after several rounds of cooperation, each learner would complete all the tasks in the map and get the corresponding scores. The design and implementation of OLP 2.0 have been highly evaluated by experts and technical staff. Teachers and learners who have adopting the platform also believe that the platform is not only convenient for teaching and learning, but also plays an important role in improving learning efficiency and creative abilities of learners.
Prediction of Students’ Performance Based on Their Interaction Logs in Online Financial Management

Online learning in financial management is becoming more common since it has the convenience and flexibility for students. How to predict students' online learning performance has become a topic of general interest in today's education research. Therefore, a new predictive model is proposed to predict students' online learning performance. Furthermore, based on students' behavior and their academic information, the proposed model can improve accuracy. The proposed model is compared with other models, and gets best experiment results. The experimental results show that the proposed model outperforms other predictive models.
Mathematical Model and Analysis of Word Attributes in Wordle Game

Wordle game is a popular puzzle game where the players need to guess a word within seven tries. In this paper, a thorough mathematic analysis on the impact of word attributes on that game has been conducted. Two features are introduced to quantify the attributes of a word. The commonness is measured by the number of occurrences in Google Books Ngram Viewer and the ease is evaluated by the number of repeated characters in that word. With the two features, a model based on gradient-boosted decision tree (GBDT) is introduced to predict the distribution of the number of tries needed to get the correct guess. The predict model has high accuracy when evaluated on test dataset. Furthermore, the commonness has been found to have more influence by comparing the importance of attributes in the model as well as calculating the Pearson correlation between them and the number of players choosing hard mode. Lastly, K-means algorithm is used to cluster the word samples and assign the difficulty labels are assigned based on the statistics of word attributes in each cluster. Such model can be utilized as way to test the difficulty of any new word.
Research on Data Rights Confirmation Mechanism in Data Sharing

Data, a new type of asset, has become the "new oil" that data driven organizations are willing to pay for. In some specific product manufacturing areas, the data production process runs through different stages such as product research and development(R&D), production, testing, delivery, and maintenance, and is needed by various task driven organizations. However, these data are born with the contradictory attributes of external confidentiality and internal sharing. To address the notarization and confidentiality issues of cross-domain data circulation, data rights confirmation is a key research area. This paper first elaborates on the data rights confirmation issues in this field, then clarifies the connotation and rules of data rights confirmation, further analyzes technologies applicable to data rights confirmation, and finally explores a data life cycle confirmation framework.
Adversarial Attack Generation in Quantum Data Classification

Adversarial attack aims to generate infinitesimal perturbations that are crafted to deceive well-trained classifiers, and has been extensively explored in the context of classical data. However, the generation of adversarial attacks for quantum data remains largely unexplored. This work addresses this gap by investigating the efficacy of classical adversarial attack generation methods in the realm of quantum data learning. Specifically, we implement two widely recognized adversarial attack generation techniques, namely, the fast gradient sign method (FGSM) and the Deepfool method, for generating adversarial attacks on quantum data based on Gell-Mann matrices. We further evaluate the deceptive capabilities of these generated adversarial samples on a classical classifier tasked with entanglement discrimination. Through extensive numerical simulations, we demonstrate the effectiveness of our adversarial attack generation methods in efficiently crafting adversarial attacks on quantum data, successfully fooling the well-trained classifier. The outcomes of our research not only extend the applicability of classical machine learning algorithms to the domain of quantum machine learning but also provide valuable insights into the vulnerability of quantum data.
Research on Port Throughput Prediction Based on Data Mining

This paper studies the prediction model of port throughput in China based on data mining, which has certain practical significance and theoretical value for guiding port planning, operation management and policy formulation. Based on the monthly throughput data of the top 40 ports in China from 2019 to 2022, time series clustering method is used to classify ports into four categories; For each category, the most representative ports are selected according to the distance from the centroid, and throughput prediction models are established using ARIMA, quadratic exponential smoothing, random forest, and BP neural network, and the prediction results of various ports are compared; Based on each group of representative ports, find the best prediction method and predict the throughput of the same type of port; To verify the effectiveness of the clustering and selecting representative ports to determine the prediction model used in this study, four models were used to predict all ports, and the root mean square error was calculated to evaluate the prediction effect. The results show that using different prediction models after classification is better than not classifying, and selecting representative ports based on clustering results to find the best prediction method is indeed the best prediction method for this type of performance.
Financial Fraud and Anomaly Detection Techniques: A Literature Review

Financial fraud has become a significant threat to economic development and national security, with fraudsters constantly evolving their fraudulent methods. Anomaly detection, as an important data analysis task, is used to identify data or events that deviate from expected behavior or are abnormal. Currently, it has been applied in the field of financial fraud detection, and scholars have shown interest in studying financial fraud behavior and related prediction techniques. This article provides an in-depth review and investigation of financial fraud and related anomaly detection techniques. It also examines the issue of scarce real data and the methods used to generate synthetic data. Furthermore, the empowerment of big data technologies to mitigate the occurrence of financial fraud is also worthy of consideration in future research.
A provable secure group key management scheme based on the Chinese remainder theorem and Pohlig-Hellman Exponential Cipher

With the emergence of diversifie services that cater to groups, efficient and secure distribution schemes for group keys are playing an increasingly important role in group communication technology. Among them, the schemes based on the Chinese Remainder Theorem (CRT) provide a more lightweight solution in terms of structure and efficiency. The existing group key management schemes, however, are not based on known hard problems and lack provable security analysis to demonstrate their security, causing potential security vulnerability. In this paper, we propose a novel provably secure master-key-encryption-based group key management (MKE-GKM) scheme based on Discrete Logarithm Problems. The MKE-GKM scheme utilizes asymmetric keys, a master encryption key, and a corresponding set of decryption keys, which are bound together by the master encryption key generation algorithm and used for the efficient distribution of the group key. Furthermore, in addition to the construction of the scheme, we also provided its provable security analysis, based on the discrete logarithm problem (DLP). The analysis indicates that the MKE-GKM scheme is provably secure and has higher communication efficiency when compared with the existing schemes with equivalent security.
Research on the Construction of Knowledge Graph for Reservoir Risk Management

Reservoir data in China is typically stored in text, PDF, and other formats, which have weak content correlation and low data structuring, making it challenging to quickly search for and retrieve pertinent data. In order to build a knowledge graph of reservoir risk management, this research suggests a deep learning-based method that involves extracting structured triples from reservoir data using deep learning models. This approach combines the top-down and bottom-up approaches to generate the ontology layer for reservoir risk management after the domain knowledge graph construction process. Following information extraction from the semi-structured data using the ontology layer, annotation tools are used to label the unstructured data. The ENIRE pre-trained model is then used to extract features from the annotated data, and then a bidirectional long short-term memory network (BiLSTM) is used to extract context semantics. The best label predictions are produced using the conditional random field (CRF). Finally, the graph database Neo4j is used to store and display the triplets of reservoir risk management knowledge. In order to complete the construction of a knowledge graph of reservoir risk management, this method was applied to reservoir data in Zhengzhou City as an example, with data preprocessing, labeling, knowledge extraction, and storage steps being implemented. This process helped to advance the development of water resource information technology in China.
A reliable density peak clustering algorithm based on delegation identification and principal clustering

Density peak clustering has gained popularity in the research community due to its simplicity, ease of implementation, and minimal parameter requirements. The way in which the DPC selects cluster centers fails to capture the characteristics of the entire dataset as a single data point can only represent a small range. This limitation often leads to issues such as multiple peaks, inaccurate identification of cluster centers, incorrect cluster numbers, and a domino effect on the distribution of remaining points. So we propose a method to identify representative and non-representative points within clusters in order to solve the above problems. Our approach involves dividing the data in each cluster into delegation and non-delegation regions. The representative points within the delegation region are then subjected to principal clustering. Finally, the non-representative points are assigned based on the representative points. Our method offers several advantages: 1) It automatically determines the number of clusters, eliminating the need for manual input. 2) We introduce new concepts of local distance and relative density, which discard the traditional cluster center selection approach. Instead, our method utilizes delegation to capture the overall characteristics of a cluster. 3) We provide a more reliable method for assigning non-representative points based on the representative points. 4) Our algorithm performs well on sparse and densely interleaving datasets. Experiments demonstrate the effectiveness of our reliable density peak clustering algorithm that incorporates intra-cluster delegation identification.
Code Association Graph Based Multi-Type Vulnerability Detection Method

In recent years, with the rapid development of Internet technology, the number of software applications has increased dramatically. In the design phase or development process, vulnerabilities generated are likely to be exploited by attackers. Fixing vulnerabilities timely at source code level can reduce the loss caused by software vulnerabilities effectively. Mainstream detection methods are complete test automation tools with manual inspection together reducing the testing workload and improving the detection efficiency. Therefore, this paper proposes a source code vulnerability detection system based on code association graph (CBVD) proved to be effective in multi-type vulnerability detection tasks. CBVD constructs the abstract syntax tree (AST) through profilers firstly, and generates control flow graph (CFG) and function call graph (FCG) by traversing tree nodes and unions three types of subgraphs to get code association graph (CAG). Then, training word2vec model to convert words files to initial graph vectors as the input of detection network. Finally, graph-level detection network orienting CBVD (CDN) is designed to detect and classify multi-type vulnerabilities. Meanwhile, putting forward representation methods of multi-type vulnerabilities and risk levels to make our detection system be not limited to binary classification tasks and more practical. Experimental results conclude that detection performance of our designed system is 0.92, better than other detection methods.
Power Grid Fault Diagnosis Based on Knowledge Graph and Bayesian Inference

This paper integrates Knowledge Graph, Scale-free Network, and Bayesian Network for fault diagnosis of the power grid. The proposed model combines Knowledge Graph based on typical accident reports with the Scale-free Network model to establish and simplify the causal network for better representing accident scenarios in the power grid. Subsequently, it constructs a Bayesian Network for quantitative analysis of accidents, from causes to consequences. Then, sensitivity analysis is conducted to provide valuable support for decision-making in power grid safety management. Based on the results of sensitivity analysis, the critical causation path of "Circuit Breaker Trip" is "Animal Intrusion"→"Improper Safety Distance"→"Two-phase Short Circuit"→"Switch Trip"→"Circuit Breaker Trip".
Research and Practice of Knowledge Support System for Chinese Medicine Use

With the development and modernization of the application of Chinese medicine, the establishment of a knowledge service system for the use of traditional Chinese medicine is currently an important research topic due to the lack of knowledge of pharmacology and medicinal properties of the inquiring patients, as well as the need for the seriousness, safety, and reasonableness of the medical use of medicines. There is an urgent need to establish a medication-centered software system to assist doctors in prescribing or conducting prescription screening, and to avoid irrational drug compounding, combination, and irrational drug dosage caused by accidental errors. Accordingly, this paper develops a knowledge support system for TCM medication, which is an intelligent software system related to TCM that is used to ensure the rationality of the medication process, to provide herbal data and standard prescription query for disease evidence, to complete the automated review and monitoring of prescriptions, and to develop a Qt-based application framework based on Qt application framework with structured programming methodology and a wide range of external application program interfaces. The research and practice of the software system can save hospital costs and improve medical efficiency while protecting patients' rights and interests.
Two-BranchTGNet: A Two-Branch Neural Network for Breast Cancer Subtype Classification

A large amount of information can be obtained from pathology images, and in order to make full use of the information on the images, we designed a two-branch neural network. One branch uses Vision Transformer to obtain pixel-level information; the other branch introduces knowledge of graph theory to construct a spatial graph structure of the tumor microenvironment and uses a graph neural network to capture spatial geometric information. For this purpose, we designed a weighted tensor feature fusion module to fuse the information of the two modalities. A smoothed labelling loss function was used to improve the accuracy of breast cancer subtype classification. Compared to the single-network backbone, the model achieved an accuracy improvement of over 6% on the BRACS dataset and a 1% improvement on the BACH dataset. The dual-branch approach outperforms the single-network backbone method and effectively integrates image-level morphological features and spatial features.
Design and research of strabismus correction therapy game system based on eye tracking technology

Strabismus is a common visual disorder in humans caused by binocular monovision abnormalities or neuromuscular abnormalities that control eye movement, as well as mechanical limitations. For patients with strabismus, the eyes are not properly aligned when looking at an object; only healthy eyes can work properly, and misaligned eyes cannot be used to acquire images of objects. In view of the poor training enthusiasm of strabismus patients, this study aims to develop a strabismus training system based on eye tracking technology combined with games so as to reshape the current strabismus training method and improve treatment efficiency. This study designed three games based on the principle of strabismus training: picking up colored balls, finding numbers, and shooting. The games introduced eye tracking and control technology, and players used their eyes to achieve control of the game and precise exercise of the eyeball, so as to achieve the purpose of strabismus training. In addition, it can also track the user's eye trajectory in real-time and evaluate the user's game performance through relevant eye movement indicators combined with game scores. Before clinical application, ensuring the simplicity and versatility of the system is crucial for doctors and patients. Therefore, this study invited 20 subjects aged 23–28 to conduct user experience evaluations, and the experimental results showed that all indicators of the system were above average.
A hybrid multi-objective genetic algorithm for gene selection in microarray data

The classification of microarray data is crucial for cancer diagnosis and prognosis. However, the high dimensionality of gene microarray data often leads to suboptimal classification performance. Furthermore, only a small subset of genes in vast datasets significantly contributes to accurate disease classification, emphasizing the importance of feature selection in this domain. This study introduces a novel hybrid feature selection method, denoted as the Maximum Relevance Non-Dominated Sorting Genetic Algorithm (MRNSGA). This approach makes use of gene-gene correlations and redundancies to facilitate the initialization of the genetic algorithm population. Additionally, a mutation retry operator is incorporated into the genetic algorithm to enhance its performance. The proposed method is compared with other advanced evolutionary algorithms across 15 gene microarray datasets. The results show that our algorithm significantly improves gene classification accuracy and considerably reduces the time required for feature selection.
Investigating the Efficacy of Virtual Reality Forest Meditation as an Intervention for Test Anxiety among College Students

The primary objective of this study is to verify the effectiveness of virtual reality forest meditation as an intervention for test anxiety among college students and to investigate the impact of three factors: namely forest style, meditation state, and pranayama guidance, on the intervention effect. A total of thirty participants exhibiting moderate or higher levels of test anxiety were subjected to an induction of test anxiety followed by a virtual reality forest meditation intervention comprising various combinations of the aforementioned three factors. Participants' levels of test anxiety were measured by the TAS scores and the quantification of brainwave energy. The results of the paired sample T-test demonstrate that virtual reality forest meditation constitutes an efficacious intervention for test anxiety among college students. The results of a three-factor analysis of variance revealed that both meditation state and pranayama guidance significantly influenced the effectiveness of virtual reality forest meditation intervention, while forest style did not demonstrate a significant impact. Specifically, wandering meditation was found to be more effective than sitting meditation, and interventions with pranayama guidance were more successful than those without pranayama guidance. Therefore, it can be concluded that guided wandering meditation combined with pranayama in a virtual reality forest environment offers superior alleviation of test anxiety among college students.
Research on Home Wireless Telemedical Monitoring System for the Elderly Based on Internet of Things Technology

In order to promote the popularization of old-age care and solve the social problems such as inconvenient physical examination for the elderly at home and insufficient number of old-age care institutions, a home-based wireless remote medical monitoring system for the elderly based on Internet of Things technology was designed. The system is mainly composed of three parts: ZigBee sensor network, 4G network and monitoring center, which can complete real-time positioning of the elderly at home and remote monitoring of body temperature, blood pressure, pulse and blood sugar. The experimental results show that all the health indexes of the four elderly people are normal. The system is stable, reliable and practical. The realization of this system can well solve the pressure faced by the current old-age care, change the traditional old-age care mode, and has high guiding value for the gradual formation and popularization of the wireless remote care old-age care system. The system realizes the wireless remote medical monitoring of the elderly at home, which has high practicability and good application prospect.
The current state and prospects of artificial intelligence in the detection of bipolar affective disorder.

Objective: Bipolar affective disorder is a prevalent mental health issue, carrying significant importance in the accurate diagnosis of depression, as early intervention and effective treatment are crucial. With the constant evolution of artificial intelligence technology, its application in the detection of bipolar affective disorder is gradually demonstrating progress. This paper aims to review the current state of artificial intelligence in the detection of bipolar affective disorder, encompassing applications in emotion analysis, data mining and predictive models, speech and language recognition, as well as remote monitoring and support. Furthermore, we will explore the challenges currently faced and delve into the future directions of artificial intelligence in the detection of bipolar affective disorder. Through this comprehensive review and research outlook, we aspire to deepen understanding of the potential and role of artificial intelligence in the detection of bipolar affective disorder, and provide guidance for future research and development.