Reviewerly/exHarmony huggingface dataset:
Study on Automatic Code Summary Generation Method based on Graph Neural Network

Automatic code summary generation techniques can help developers and other technical personnel to understand and comprehend the functions and structure of code more quickly. In existing methods of automatic code abstraction generation, there is a problem of poor utilization of code structure information, therefore, a model of automatic code abstraction generation based on graph neural network is proposed. Firstly, the abstract syntax tree is extracted from the code, and then the abstract syntax tree of the code is transformed into a graph representation using control flow statements in the data stream, which is trained by a graph convolutional neural network, and the obtained training results are fused with the original sequence representation to finally obtain a source code representation that incorporates the semantic information of the code context and structural information. Meanwhile, to improve the stability of the summary generation model, a deep learning model based on a multi-headed self-attentive mechanism is used to improve the existing sequence-to-sequence summary generation model. The accuracy and stability of the model-generated summaries are improved. Validation was performed on publicly available datasets, and experimental results show that the method achieves better results on BLEU-4, METEOR, and ROUGE-L metrics compared to other baseline models.
Construction of Teaching Quality Evaluation Model of English Flipped Classroom Based on BP Neural Network Algorithm

The English SPOCS(Small Private Online Courses) teaching model has had a profound impact on the reform of higher education. The teaching mode of teaching and learning has been transformed into the inquiry teaching mode of problem-centered, learning first and teaching later, which has been favored by the majority of teachers in China. As a new teaching model, SPOCS involves all the main aspects that should be included in the evaluation index. In this paper, the BPNN evaluation model is trained to evaluate the teaching quality of English SPOCS more scientifically, effectively and objectively, and to provide a reference basis for the reform of English SPOCS teaching and the improvement of teaching quality. Therefore, this paper analyzes the teaching quality of English SPOCS based on BP neural network algorithm, and constructs a teaching quality evaluation model. Finally, the simulation results show that the indicators that have an important impact on the evaluation results are selected and used as the input of BPNN(BP Neural Network). At the same time, the dimensions, levels and weights of the evaluation indicators should be reasonably allocated. The evaluation indicators should be relatively independent, reflecting the characteristics of SPOCS and the actual situation of English teaching in higher vocational colleges.
Research on Medical Image Data Sharing Traceability System Based on Blockchain

Because medical image data involves patient privacy, the sharing of medical image data must protect patient privacy and ensure that the data is accurate and not tampered with, and it is a challenge to achieve medical data sharing system that is both shareable and secure for the above reasons. At present, medical image data sharing systems generally face problems such as privacy leakage and lack of effective traceability technology. This paper designs a blockchain-based medical image data sharing and traceability system, which improves the drawbacks of the traditional medical image data sharing system with the help of the technical characteristics of blockchain, such as non-tampering, security and transparency, traceability and decentralization. The system uses the alliance chain to introduce an authentication mechanism, and only authorized nodes can join the network, which limits the scope of data use, which is equivalent to enhancing the security of data. The traceability of blockchain is used to query the source of medical image data and the use and modification records of medical image data in the system to avoid illegal tampering or abuse of data, and effectively improve the security and reliability of the shared traceability system.
Research on Optimization Strategy of Data Skew Problem Based on Hive Partitioning Mechanism

Due to the complex structure and large scale of big data, there are also data skew problems caused by uneven data distribution and the limitations of default partitioning algorithm, which is also a common performance bottleneck in distributed computing, and seriously affects the overall resource utilization of the system. Based on the execution principle of MapReduce, this paper proposes Hive based load balancing methods such as random prefix, preaggregation, parameter tuning, etc., in order to improve computing parallelism and reduce application completion time.
Employment Guidance and Service for College Students Based on Python Technology in Big Data Environment

With the rapid development of information technology and Internet technology, we have entered an era of big data, and now the employment pressure of college students is increasing, and more and more students are beginning to seek better employment guidance and services. This paper proposes a method of employment guidance and service for college students based on Python technology. First, a method for obtaining employment information based on Python technology is proposed. The second is to use python technology and multi-attribute decision-making theory to realize scientific and efficient decision-making guidance methods, aiming to help college students better prepare for job interviews and improve employment competitiveness through information technology.
Network Platform Education Resource Development Process Construction Based on Cloud Computing Technology

The development of cloud computing technology promotes the process of education informatization and opens up new ways and ideas of teaching. Internet-based cloud computing is a computer technology that provides users with reliable computing services based on open standards and services. The concept of cloud computing has been predicted two years ago to the fact that cloud computing has been successfully applied to the banking industry, telecommunications, government departments and other aspects of the society, cloud computing as information technology as a key trend in the future development and a new generation of information technology in an important pillar industry of an important direction. Supported by cloud computing, educational resource sharing service platform can effectively cope with the problems of excessive investment in infrastructure and low utilization rate of educational resources, which is the key to promoting educational resource sharing and inter-regional exchanges and cooperation in higher education. Various educational cloud applications can be flexibly deployed to achieve centralized management of massive educational cloud resources, so that multiple users can transparently obtain the required educational cloud services, and solve the practical problems of uneven distribution and difficult sharing of educational resources.
Research on the Influence of Blended Writing Teaching Model Based on data mining on College Students’ English Writing Ability

With the rapid development of multimedia technology, digital teaching mode is gradually popularized. As a brand-new network teaching mode, blended teaching has become the mainstream of information teaching. The introduction of blended teaching mode not only realizes the integration of online learning and face-to-face learning, but also improves undergraduate' learning enthusiasm and initiative and learning efficiency. In view of the fact that non-English major undergraduates' English Composition ability is insufficient and the special teaching hours of writing in class are limited, our research team makes full use of information technology to study the mixed teaching mode of "teachers publish teaching plans and learning contents through video courseware in virtual space, guide undergraduate to complete autonomous learning, and organize small class discussions in physical space", develops a shared class of college English Composition, and sets up a study group among undergraduate to conduct practical research. The implementation of mixed teaching can promote the writing level of undergraduate in the experimental class; It can have a positive impact on the writing of undergraduate at different levels, and can also mobilize undergraduate' enthusiasm for writing and give play to their writing potential. The author also summarizes the shortcomings of this study and analyzes the reasons, and draws research inspiration, with a view to contributing to the future research in related fields.
LX-BBSCA: Laplacian biogeography-based sine cosine algorithm for structural engineering design optimization

&lt;abstract&gt; &lt;p&gt;In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms.&lt;/p&gt; &lt;/abstract&gt;
The Basics of Machine Learning

This chapter offers a general introduction to the rationale and ontology of Machine Learning (ML). It starts by discussing the definitionMachine learningdefinition, rationaleMachine learningrationale, and usefulnessMachine learningusefulness of ML in the scientific context. Then, it underscores the transition from symbolic AISymbolic AI to statistical learningStatistical learning and examinesCurse of dimensionality the curse of dimensionality as the main source of the non-identifiability of the mapping between the features and target variable in supervised Machine Learning. Next, the chapter provides a guiding taxonomy of Machine Learning methods and discusses some ontological aspects related to Machine Learning as a scientific paradigm. A few final remarks end the chapter.
Sentiment Analysis

This chapter presents the theory and practical applications in Stata, R, and Python of the so-called sentiment analysis (SA), a subfield of Machine Learning called natural language processing (NLP). The SA main purpose is to produce a predicting mapping between human textual expressions (generally stored in documents such as emails, web pages, electronic documents, etc.) and specific human feelings. For this purpose, SA integrates text mining and statistical learningStatistical learning for sentiment predictionPrediction. After presenting the SA definition and logic, this chapter presents the procedure of text vectorization through the ”bag of words” algorithm, which is key for transforming textual documents into datasets. We propose three softwareSoftware applications: (1) classifying the topic of newswires based on their text (Stata); (2) detecting positive/negative sentiment toward movies based on people’s reviews (R); building an email “spam” detector (Python).
Deep Learning

This chapter presents deep learningDeep learning algorithms, a subset of machine learning methods built on sophisticated multi-layer artificial neural networks (ANNs)Artificial neural networks. The “deep” labeling has mainly to do with the construction of ANNs characterized by a hierarchical stratification of layers that stand between the observed inputs and the observed outputs. The chapter starts by providing some intuition of why deep learningDeep learning models can have high predictive performance, focusing on the concept of data ordering. Next, it presents in detail two classes of (supervised) deep learningDeep learning models that have become popular in the literature: (i) convolutional neural networksConvolutional neural networks (CNNs), and (ii) recurrent neural networks (RNNs)Recurrent neural networks. CNNs have proved to be particularly successful for imaging recognition, while RNNs for sequence predictionPrediction (including both quantitative and qualitative sequences). The applied part of the chapter presents applications in Python of these two deep learningDeep learning architecturesLearning architecture using the KerasKeras API.
Artificial Neural Networks

This chapter treats artificial neural networks (ANNs), a simplified computational representation of a biological neural system constituting the brain of humans and animals. It starts with a formal definition of what an artificial neural networkArtificial neural networks is, as well as how to fit it to the data. Here, we present the popular gradient descentGradient descent approach based on the back-propagation algorithmBack-propagation algorithm to fit an ANN. The chapter goes on by outlining two notable ANN architectures, that is, the PerceptronPerceptron and the AdalineAdaline. The applied part of the chapter, finally, presents some ANN implementations using Python, R, and Stata.
SecureSL: A Privacy-preserving Vertical Cooperative Learning Scheme for Web 3.0

Web 3.0 is the highly anticipated new evolution of the World Wide Web that promises greater decentralization, intelligence, and security than its predecessors. Neural networks are a powerful tool that can be used to build more intelligent and personalized web. However, traditional centralized learning of neural networks is limited, and the emerging cooperative learning methods such as federated learning are often restricted to horizontally partitioned data. To enable vertical cooperative learning and tackle the associated privacy preservation issues, we propose SecureSL, a vertical cooperative learning scheme based on split learning. We present an edge-end cooperative learning framework that fits the decentralized feature of Web 3.0. We adopt multi-key homomorphic encryption (MKHE) to ensure the confidentiality of the data, the labels and the trained model, making SecureSL resistant to collusion attacks. Furthermore, we employ the single instruction, multiple data (SIMD) technique and optimize the training process to improve the computational efficiency of MKHE. Finally, we evaluate the privacy preservation performance and the model accuracy of SecureSL through extensive experiments. The results show that our proposed scheme provides a robust solution for vertical cooperative learning in Web 3.0 applications which can address the critical privacy concerns while maintaining the model accuracy.
Attention Relational Network for Skeleton-Based Group Activity Recognition

Group Activity Recognition refers to multi-individual activity analysis. It is a significant and challenging task in computer vision. The solution of group activity prediction can be classified with traditional hand-crafted features, RGB video features, and skeleton data-based deep learning architectures, such as Graph Convolutional Networks (GCNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTMs). However, not only are those solutions usually designed to be complex, but they also rarely explore pose information and rarely use relational networks to reason about group activity behavior. They disregard the magnitude and orientation details of the skeletal edges, crucial for action recognition, potentially leading to suboptimal outcomes in these methodologies. In this work, we leverage minimal prior knowledge about the skeleton information to reason about the interactions from group activity. The objective is to obtain discriminative representations and filter out some ambiguous actions to enhance the performance of group activity recognition. Our contribution is a proposed Attention Relation Network (ARN) that fuses the attention mechanisms and joint vector sequences into the relation network. The skeleton joints vector sequences are previously unexplored pose information and assign greater significance attributed to individuals who are more relevant for distinguishing the group activity behavior. In the first stage, our model focuses on the specified edge-level information (encompassing both edge and edge motion data) within the skeleton dataset, considering directionality, to analyze the spatiotemporal aspects of the action. In the second stage, recognizing the inherent motion directionality, we establish diverse directions for skeleton edges and extract distinct motion features (including translation and rotation information) aligned with these various orientations, thereby augmenting the utilization of motion attributes related to the action. We also introduce a representation of human motion achieved by combining relational networks and examining their integrated characteristics. Extensive experiments were tested in the Hockey and UT-interaction datasets to evaluate our method, obtaining competitive performance to the state-of-the-art. Results demonstrate the modeling potential of a skeleton-based method for group activity recognition.
ESASCF: Expertise Extraction, Generalization and Reply Framework for Optimized Automation of Network Security Compliance

Organizations constantly exposed to cyber threats are compelled to comply with cyber security standards and policies for protecting their digital assets. Vulnerability assessment (VA) and penetration testing (PT) are widely adopted methods for security compliance (SC) to identify security gaps and anticipate security breaches. However, these methods for security compliance tend to be highly repetitive and resource-intensive. In this paper, we propose a novel method to tackle the ever-growing problem of efficiency in network security auditing by designing and developing an Expert-System Automated Security Compliance Framework (ESASCF). ESASCF enables industrial and open-source VA and PT tools to extract, process, store and re-use the expertise in similar scenarios or during periodic re-testing. ESASCF was tested on different size networks and proved efficient in terms of time efficiency and testing effectiveness. ESASCF takes over autonomously the SC in re-testing and offloading the human expert by automating repeated segments SC and thus enabling experts to prioritize important tasks in ad-hoc compliance tests. The obtained results show a performance improvement by cutting the time required for an expert to 50% in the context of typical corporate networks’ first security compliance and 20% in re-testing. In addition, the framework allows a long-term impact illustrated in the knowledge extraction, generalization, and re-utilization, which enables better SC confidence independent of the human expert skills, coverage, and wrong decisions resulting in false negatives.
PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Mental health is vital for emotional, psychological, and social well-being. Mental illness can affect thoughts, feelings, and behaviors. Early intervention and specialized care can manage major mental illnesses. Predicting mental state accurately can facilitate behavioral changes and promote overall well-being. The paper proposes a novel personalized time series model called PredictEYE, which aims to predict a person’s mental state and identify the specific scene responsible for that mental state. The model achieves this by analyzing individuals’ eye-tracking time series data while watching calm and stressful videos. The model utilizes deep learning time-series univariate regression model based on Long Short-Term Memory for predicting the future sequence of each feature and a machine learning-based Random Forest algorithm for the mental state prediction. The model’s performance was compared across the state-of-the-art literature survey. The predictEYE model could achieve an accuracy of 86.4% accuracy in predicting mental state. Tailoring eye tracking models to individual differences is more effective in comprehending mental states than models that make comparisons across multiple participants, given eye tracking data’s unique and distinctive idiosyncratic nature. The eye tracking features play a crucial role in predicting the mental state, and the model is adaptable to work with webcam-based eye tracking and can relate to applications where continuous and non-invasive monitoring is required.
Enhancing Biometric Speaker Recognition Through MFCC Feature Extraction and Polar Codes for Remote Application

While extensive research has been conducted in the field of biometrics, particularly in face and fingerprint recognition, remote speaker recognition has yet to gain global acceptance due to challenges related to accuracy and data integrity. Previous studies in speaker recognition have explored techniques such as Mel Frequency Cepstral Coefficients (MFCC) and Convolutional Neural Networks (CNN), yielding accuracy rates of 90.4% and 92.8%, respectively over a fixed and small database but on a standalone system. To address the data integrity and accuracy issues for enhancement in remote speaker recognition, a novel approach is proposed in this paper. Initially, remote speaker recognition is implemented using a client-server setup, but the presence of channel noise hindered any noticeable improvement in accuracy compared to existing methods. The new approach involves extracting MFCC parameters from voice samples and subsequently applying polar error-correcting coding techniques for storage as well as transmission to achieve fidelity. Using a code rate of ½ and a block length of 1024 bits, the transmission of polar-coded MFCC features over a noisy channel yielded a lower bit error rate when coupled with successive list decoding. Simulation results demonstrate a reduction in bit error rate, resulting in an accuracy of 95.2% in the implemented remote speaker recognition system. This represents a significant 5% improvement over the existing standalone system that uses MFCC alone and a 2.5% enhancement over the combination of MFCC and CNN. These findings highlight that the Polar codes can be effectively utilized in speaker recognition systems to enhance their robustness and reliability, especially in scenarios with noisy channels or challenging conditions.
Response to COVID-19 Pandemic and Ethical Innovations in Taiwan

Taiwan has established itself in recent decades as a democracy that values and protects human rights. By extension, the rights and well-being of research subjects are clearly defined through legislation. Two notable cases detailed here are the Biobank Act and the law concerning research involving indigenous people. During COVID-19, however, citizens were quick to surrender their privacy and freedom in support of intrusive public health measures that aimed to combat the pandemic. This research investigates the reasons behind this swift shift in general public’s tolerance toward intrusion of privacy and limitation of freedom. The results show that Taiwan’s public health history, social and geopolitical background, and geographical and demographic characteristics all played a role in successful COVID-19 policy compliance. Additional factors contributing to the country’s effective management of COVID-19 were innovative contact tracing design and proactive communication strategies.