Reviewerly/exHarmony huggingface dataset:
A Framework of Chinese Vocabulary Smart Fragmented Learning System

Fragmented learning is a learning mode that relies on a ubiquitous learning environment in the era of digital education. Vocabulary learning is a kind of weak systematic accumulation learning task and the fragmented learning mode is suitable for it. In the era of smart education, learning environment perception technology can enhance the environmental suitability of learning resource recommendations in fragmented learning. This paper proposes a framework of Chinese Vocabulary Smart Fragmented Learning System (CVSFLS) which supports Chinese vocabulary fragmented learning driven by idioms learning. AI-based environmental analysis technology, mobile positioning technology, vocabulary knowledge graph and learning resources with learning environmental information enable this system to support smart learning. Fun, multimodal learning resources and an unlimited list of recommended words make learners more addicting in fragmented learning.
AI Interaction Design Driven Software Engineering: An Exploratory Experimental Teaching Method

The Human-centered Artificial Intelligence (HAI) report shows that it is crucial to overcome the challenge of using AI technology effectively and responsibly for constructing intelligent systems. To address these challenges, an exploratory experimental teaching method driven by an AI interaction architecture designer is proposed. This teaching method involves two phases. In the first phase, interaction designers analyze user interaction scenarios and design the user experience, including interaction modeling and user interface prototyping. In the second phase, software engineers use AI methods to develop the intelligent system based on the user interface prototype. The AI interaction architecture designer plays a critical role in deciding the appropriate AI methods to meet user needs. The success of this teaching method is demonstrated by the successful delivery of intelligent systems. This method can enable effective and responsible use of AI technology in building or refactoring intelligent systems. Therefore, this teaching method has been validated.
A Study on Random Differentiation Methods for Homework Based on Fuzzy Test

A fuzzy test-based method for random differentiation of homework is proposed to address the problems of low update frequency, serious plagiarism and weakened barriers in university coursework. In order to generate fuzzy homework questions, a semi-automatic framework for fuzzy test-based homework is designed and the four key steps are described in formal language, including pre-processing, variable construction, variable screening and difficulty assessment. The effectiveness of the method is illustrated through a comparison of student teaching data, questionnaire feedback and faculty interviews in a pilot course on principles of computer composition.
Impact of EIP-1559 on Transactions in the Ethereum Blockchain and Its Rollups

EIP-1559 is an Ethereum Improvement Proposal that aims to modify the transaction fee system on Ethereum and has also been adapted by some of its layer 2 Rollups. It would reduce fees paid by its users and help make its cryptocurrency a deflationary currency by burning a certain amount of it during operations. However, this modification does not only bring advantages. In this paper, we conduct a practical study to show how it affects the transactions on the Ethereum Blockchain as well as its layer 2 scaling solutions (Rollups) Polygon and Arbitrum in term of gas fees and waiting times.
Pseudonym Swapping with Secure Accumulators and Double Diffie-Hellman Rounds in Cooperative Intelligent Transport Systems

The notion of pseudonym certificates is used for anonymity and privacy of vehicles in Cooperative Intelligent Transport Systems. Due to attacks where an adversary can link a certain vehicle to a geographical bounded area or usage of services, there is a need to protect the vehicle’s real identity, but at the same time being able to prove its existence at a later stage due to auditing. We propose a pseudonym swapping protocol aligned with a new security architecture where signature keys are stored in both a typical secure hardware module and in a secure accumulator that provides proof for key validity. We propose minor adjustments to the current ETSI standard to include such secure setup and provide a comparative proof of concept implementation between two variants of secure accumulators.
Towards a Secure Cross-Blockchain Smart Contract Architecture

With the speed rise of heterogeneous Blockchain (BC) platforms, interoperability became a critical area of research barrer for a variety of use cases, ranging from supply chain to healthcare. Consequently, a number of BC interoperability solutions were proposed to overcome these issues. One of these is Bifröst, a notary-based BC interoperability Application Programming Interface (API) which enables users or existing applications to interact seamlessly with multiple BCs. An early prototype of Bifröst was built, which proved its feasibility, nonetheless, it failed to allow interactions with Smart Contracts (SCs) across BCs. In this context, we present in this paper an extension of Bifröst architecture enabling secure cross-BC SCs using the Trusted Execution Environment (TEE). The proposed architecture is also fault tolerant by means of a logging module that facilitates the recovery of partially completed transactions in case of a crash.
AI Translation Quality Evaluation of Attributive Clauses Based on Faithfulness, Expressiveness and Elegance Principle

The widespread use of AI translation has made AI translation quality assessment an increasingly important topic, but related researches need deepening. There exist no sufficient studies comparing human translation and AI translation through statistical analysis. This paper compares human translation, Google translation and Youdao translation of attributive clauses. Data analysis of the online questionnaire shows that the quality of human translation is higher than that of AI translation. The quality of Google English-Chinese translation is higher than that of Youdao. Qualitative analysis is under the guidance of “Faithfulness, Expressiveness and Elegance” principle and the BCC corpus. The qualitative analysis results verifies the results above. Compared with Youdao, Google has better control over the translation methods of attributive clauses, even closer to the expression of human translation. Preposition translation, substitution of pronouns for antecedents, repeated antecedents, additional translation methods are used in line with the original text. However, Google translation is not perfect in some aspects and may neglect some Chinese expression habits. Occasional mistranslation of words or neglect of some Chinese idiomatic collocations are found. Sometimes nouns cannot be flexibly translated into verbs according to Chinese language rules in Google translation.
IoT Security Within Small and Medium-Sized Manufacturing Companies

Digitization of production is on the strategic agenda of many companies across the globe. The term IoT (Internet of Things) is meanwhile common for small and medium-sized companies (SMEs). Along with the technical innovations, especially in the environment of cloud based IoT solutions, the topic of IoT security is gaining outstanding importance. This paper presents a pre-study and investigation of the experience, awareness, activities, and knowledge regarding IoT security among SMEs. Despite of the limited numbers of respondents (29) the results of the research show that while SMEs are aware of IoT security threats, they also lack the knowledge to protect themselves against all possible attack vectors.
Clinical Intelligent Interactive System Based on Optimized Hidden Markov Model

In recent years, the relationship between doctors and patients in hospitals has been relatively tense. Doctors often comfort patients in hospital on the one hand, and manually record various information on the other hand. In order to facilitate the recording of nursing information and facilitate doctors’ ward rounds, the hospital needs to establish a clinical intelligent interactive system. The system can use voice to input information in the case of inconvenient manual operation. For the speech recognition module in the system, the acoustic model for speech recognition based on Hidden Markov Model is used, and by combining the specific conditions of the hospital to collect the speech of all doctors, the interaction efficiency between doctors and patients is greatly improved.
Comparison Between Methods Based on LSTM and BLS for Stock Analysis and Prediction

Broad Learning System (BLS) is a new model to expand the neural network from a horizontal perspective in recent years. Based on its outstanding performance in various aspects, this paper intends to apply it to the analysis and prediction of the stock market. Stock market data is a kind of time series data with strong correlation in time. After analyzing and summarizing the advantages and disadvantages of various existing time series prediction and analysis methods, this paper selects the LSTM (long - short term memory network) model, which is outstanding in time series prediction, to compare with the BLS model, and to examine the ability of these two methods in stock market data analysis and prediction. The experimental results show that the cyclic BLS system also has good performance in the prediction of time series data, especially in reducing the training time. The main reason is that the weight of each layer of LSTM is updated by gradient layer by layer, while the weight from hidden layer to output layer in BLS model is solved by pseudo-inverse calculation, which avoids the gradient update method and ensures the efficiency of network training. In addition, the BLS-based loop structure is adopted in this paper. The nodes in the feature layer or enhancement unit are connected circularly. The nodes can capture the dynamic characteristics of the time series, and well acquire and calculate the time-related information in the time series.
Design of Smart Mistake Notebook Based on AI and Big Data

Under the general direction of information technology in education, traditional teaching methods are gradually integrating with intelligent technology. The notebook used for recording past mistakes on exams or exercises will be referred to as the “mistake notebook”, which has been proved to be an effective learning tool in practice, but due to its difficulty in recording, retrieval and low efficiency, it also needs to be transformed into intelligent and electronic. The research group has proposed and designed a smart mistake notebook based on AI and big data. By automatically collating and analysing students’ mistakes, it will help students to use the mistake notebook more efficiently and review more effectively and accurately to improve their academic performance.
Asian Games Service Intelligent System Based on Privacy-Preserving Recommendation

As the Hangzhou Asian Games is approaching, the research group has designed and implemented an intelligent system for Asian Games services with privacy-preserving recommendation system, aiming to solve the problem that people cannot obtain personalized Asian Games information. The system adopts Term Frequency Inverse Document Frequency (TF-IDF) and the privacy enhanced matrix factorization to realize the multi-criteria privacy-preserving recommendation. The system can meet the individuals’ needs to obtain sports event information with the protection of user privacy. It improves the users’ experiences, meanwhile, conduce to the spread of Hangzhou and the Asian Games.
Visualization Analysis on the Research Status and Trend of Recommendation System

In order to fully recognize the research status and development trend of the recommendation system, by using the software CiteSpace, taking reference to the literature in the recommendation system from 2012 to 2021 collected by the database Web of Science, this paper is to analyze the overall distribution characteristics on the years, journals, countries/regions, institutions/authors, important documents, research hotspots/research frontiers via the literature statistics and the visualization. As the result, there is going to be a knowledge map to be drawn to provide references for the researchers of the recommendation system.
Research on Workflow Model and Verification Method of Remote Sensing Data Processing

To reduce the uncertainty factors in the remote sensing data processing process and the complexity of remote sensing data processing software, a remote sensing data processing workflow model is presented based on Petri net and UniNet theory. The model can not only describe the data, tasks and their logical relationships involved in remote sensing data processing workflow formally, but also analyze the logic of the process dynamically. Furthermore, the model can be effectively combined with workflow technology, which provide great help for solving remote sensing application needs.
Assessing the AWE-Based Teacher-Assisted Feedback Model for College English Writing Teaching at the Application-Oriented University

The Ministry of Education in China promotes the full use of high-quality teaching software and teaching resources to deepen the reform of college English teaching for establishing new teaching models. Scholars generally agree that the automated writing evaluation (AWE) is feasible in English writing teaching; however, the auto-recognition of error categories needs to be improved. Previous studies have mostly focused on its application and reliability, and few have examined the AWE-based teacher-assisted feedback mechanism. We aim to examine and evaluate the feasibility and effectiveness of the mechanism by the case study of an AWE platform in China via a teaching experiment, corpus extraction from misjudged errors and its statistical analysis. The specific research questions to be addressed are: 1) What types of AWE misjudgments are actually manifested, and 2) Can the AWE-based teacher-assisted feedback model compensate for its shortcomings? It argues that under the AWE-based teacher-assisted feedback mechanism, 1) misjudgments of complex semantic recognition, complex sentence recognition and the auto-recognition of content words are likely to be commonly-shared features of the AWE platforms used in application-oriented universities in China, thus providing a general direction for operating the targeted teacher-assisted feedback mechanism; 2) the submitted times and the scoring indicates a linear correlation between the number of students’ submitted times and their final scoring, and this correlation is particularly evident for individual students; when the EFL learners received the writing program under the integrated feedback mechanism, their writing and translation increased accordingly by encouraging multiple submissions, scoring refreshments, multiple feedback by means of online, offline and teacher’s artificial involvement; 3) the AWE still needs to be optimized with respect to the scale of corpus data, multiple error correction algorithms, i.e., the grammatical error correction algorithms based on language models, machine translations and grammar rules.
Short Text Dynamic Clustering Approach for Semantic-Enhanced Knowledge

Traditional text clustering algorithms largely rely on word co-occurrence information in text data to infer the hidden topics. However, due to the limited content length of the short text, the word co-occurrence information in the short text is very scarce, which we call the short text feature sparse problem. In order to solve the feature sparse problem in the dynamic clustering of short texts, and more better capture the dynamic evolution of topics in the dynamic short text data over time, this paper proposed a semantic-enhanced dynamic Dirichlet multinomial Mixture (SDDMM) model with enhanced semantics, which uses the additional semantic knowledge provided by word embedding to assist in improving the effect of short texts’ dynamic clustering, at the same time, because the generation of topics in the dynamic clustering process is affected by inherited historical topics, the introduction of semantic knowledge can automatically adjust the strength of topic inheritance, making the dynamic evolution of the number of clusters more in line with the actual data. Experiments on synthetic data and real data show that the SDDMM model effectively improves the short texts’ dynamic clustering effect.
Virtual Private Network Blockchain-based Dynamic Access Control Solution for Inter-organisational Large Scale IoT Networks

Access control management in IoT environment faces big challenges. Unfortunately, it is hard to implement current access control standards on smart objects due to the complexity of access control solutions and the limited capacities of IoT devices. The problem becomes harder when these IoT devices belong to different organisations and need to interact together frequently. In the last few years, blockchain technology has been widely used in inter-organisational access control solutions for IoT. In fact, this technology can ensure an efficient and distributed access control. However, the existing solutions require lot of time and resources. In this paper, we provide a solution for a secure access control management in inter-organisational IoT networks based on blockchain and Virtual Private Network (VPN) technology. To optimize the resources and the time needed for access control, the IoT devices that belong to different organisations and cooperate frequently are added to the same VPN. The access control is ensured per VPN using blockchain. The experimental results of our proposed approach have shown the efficiency of our proposed approach in offering a secure, real-time response, decentralized, scalable and lightweight access control solution.
FedHD: A Privacy-Preserving Recommendation System with Homomorphic Encryption and Differential Privacy

To solve the problem of leakage of user privacy information in traditional recommendation systems, this paper proposes a federated learning recommendation system framework with homomorphic encryption and differential privacy, called FedHD. While protecting the privacy of users’ personal information and interaction information, a personalized mask filling is designed, which ensure the performance of the overall recommendation system and keeps the balances between the privacy protection and time consumption of the overall system. Experiments on three real-world datasets show that FedHD can meet the real-time accuracy of the recommendation system while achieving privacy protection.
Analysis and Conclusion: Children’s Safety Education Games Based on VR

The problems of traditional safety education games, such as boring content, single scene, poor sense of experience and immersion, are gradually emerging. This paper analyzes and sums up six different kinds of safety education games, and leads to the advantages of safety education games with VR technology. This paper analyzes the application advantages and general design principles of VR technology in children’s safety education games from the angles of immersion, interactivity and situational style, summarize the future trend of children’s safety education games based on VR technology.
Facial Expression Image Classification Based on Multi-scale Feature Fusion Residual Network

Due to the peculiar subtlety and complexity of facial expressions, only using the depth features of the network will ignore the complementarity between the multi-layer features, resulting in the loss of effective information. A facial expression image classification model based on a multi-scale feature fusion residual network is proposed to aim at these problems. Firstly, the ResNet-50 network structure is selected for improvement, with the dilated convolutional extraction introduced to extract the multi-scale features in the neural network; design a new two-branch multi-scale feature fusion module, using multiple deep features to fuse shallow features in parallel to obtain finer-scale abstract information, showing the advantages of high-performance model training while having efficient inference speed; the 3-time full connection and classifier have been packed to strengthen the consistency of feature map and category and enhance model generalization. The improved network model is trained in RAF-DB, JAFFE, and Oulu-CASIA datasets. According to the experimental results, the network obtains an accuracy of 85.79%, 96.65%, and 97.29% in the classification of facial expression images, respectively, which is higher than other advanced methods and can serve as a reference for how to conduct facial expression image classification.
ANFIS Domestic Water Consumption Model Before and During Covid19 Pandemic in Tangerang Indonesia

WHO has declared Covid-19 disease as a pandemic on a global scale. The Indonesian government had announced a physical distancing & movement control order (PSBB) to combat Covid-19 transmission. The effect of PSBB caused many factories to stop operating and forced workers to work from home. This event had resulted in the increase in domestic water consumption. In this paper, we analyze the increased domestic water consumption using ANFIS mathematical model. The model shows the increase in domestic water consumption three months before and the first three months of the Covid-19 pandemic, due to PSBB, Moslem religious rituals such as fasting in Ramadhan, and the ban of Homecoming in Eid Fitr. We can see the model shows significant increases in domestic water consumption from 3 constant variables such as: A from 1,243,000 to 1,288,000, B from 1,303,000 to 1,410,000, and C from 1,279,000 to 1,340,000.
An NI-PSO-LightGBM Model Based on Adversarial Validation: Mortality Prediction in ICU Diabetic Patients

The number of diabetic patients and mortality rates are increasing year by year, imposing a heavy burden on health economies and families. ICU mortality prediction is crucial for patient care and allocating hospital resources. In the paper, a NI-PSO-LightGBM diabetic inpatient mortality prediction model based on adversarial validation is developed. The model was trained and tested using MIT's GOSSIS dataset provided from MIT, which gives various factors involved in hospitalization of diabetic patients. Based on these factors, patients are predicted to survive or not, and the dataset has 91,713 samples with 84 features. Adversarial validation was used to partition the dataset to ensure an even distribution of the training and test set samples and to avoid the model being much less effective on the test set than on the validation set. Feature extraction was performed on the samples using Null Importance to find the optimal subset of features containing the most information. The particle swarm optimization algorithm(PSO) was introduced to adjust the hyperparameters of the LightGBM model to obtain the final NI-PSO-LightGBM model and compare it with the Bayes-optimized LightGBM model (Bayes-LightGBM). The experimental results show that for the Bayes-LightGBM model, the average AUC = 88.66% for 5-fold cross-validation on the validation set, AUC = 88.88% and ACC = 92.84% on the test set. For the NI-PSO-LightGBM model, the validation set Average AUC = 89.52% for 5-fold cross-validation, AUC = 89.53% and ACC = 93.09% on the test set. The difference between the AUC values of the two models in the validation and test sets is extremely small, indicating that the adversarial validation prevents overfitting, and the NI-PSO-LightGBM proposed in the paper performs better than Bayes-LightGBM in both validation and test sets in terms of AUC and ACC, indicating that the NI-PSO-LightGBM model has better predictive ability.