Reviewerly/exHarmony huggingface dataset:
Production Scheduling Optimization of An Aviation Bearing Manufacturing Enterprise Based on Teaching-Learning-based Optimization

This article is based on an aviation bearing manufacturing and wind power bearing manufacturing enterprise production scheduling for its key process optimization, in Qingdao, Shandong. The production scheduling is aimed at the shortest delivery time, and the production scheduling assessment indicator is the on-time delivery rate of orders. This article, based on the production process and production mode characteristics of the enterprise, improves the convergence ability of the Teaching-learning-based optimization (TLBO) in the early stage and the detail search ability in the later stage by optimizing the static teaching factor function and algorithm retrieval method of the classic algorithm. By combining Levy flight to further enhance the algorithm's global search ability and reduce the probability of falling into local optima. The theoretical reliability of the algorithm was verified by comparing the calculation result of the MK calculation example with other algorithms. The reliability of the algorithm in actual production was verified through the long-term order execution data and order delivery rate of the enterprise.
Research on the Compilation of Idiom Entries in Specialized Chinese Textbooks

Since the "the Belt and Road" initiative was put forward, various connections between China and the world have increased, and the value of Chinese language teaching in specialized fields has rapidly increased. As an important part of Chinese as a foreign language, the importance of Chinese idiom teaching cannot be ignored. However, at present, the academic community does not attach enough importance to the compilation of idiom entries in specialized Chinese textbooks. This article focuses on business, medicine, and art textbooks for teaching Chinese as a foreign language. It compares the inclusion of idioms in the textbooks with general and specialized outlines, and studies the degree of correlation. It analyzes the writing of idioms in specialized Chinese textbooks, summarizes the shortcomings in the writing of idiom entries in the textbooks, and proposes writing suggestions.
The Study on the Strategy for Education Management Innovation in Primary and Middle Schools under the Background of Informatization

Since entering the 21st century, the rapid development and wide application of information technology have changed people's habits of life and cognitive habits, and China's "2010-2020 Education Informatization Planning Outline" has made it clear that "information technology has a revolutionary impact on the development of education", which means that informatization also puts forward new requirements for the innovation of education management in primary and secondary schools. This means that information technology also puts forward new requirements for the innovation of education management. As an important stage of compulsory education, primary and secondary schools are developing towards informatization, technologization and diversification.
Design and Development of a Companion Toy for Children with Autism

Globally, there are more than 67 million people with autism, 40% of whom are children, and the number of children with autism in China has reached more than 2 million, with a rising trend year by year. The lack of social skills is the core deficit of autistic children, which seriously affects their social development. Early intervention treatment is especially important for autistic children. With the progress of the times and the development of technology, the research on the use of modern technology for autism intervention is increasing, and based on the characteristics of autistic children's "pro-objects", more and more intelligent products play an important role in helping autistic children's rehabilitation and education. In China, there is less research on products for autistic children, especially on intelligent products, and most of the intelligent products on the market are designed abroad. Against this background, this project explores the needs of autistic children in intervention therapy by studying the social interaction ability of autistic children, exploring the design strategies related to them and practicing the design from the perspective of smart companion products guiding the social intervention of autistic children.
From Roots to Fruits: Exploring Lineage for Dataset Recommendations

Our research article presents a recommender system for datasets, models, and processing steps that is based on utilizing metadata characteristics, content, and usage history to understand the intent of artifacts in a data lineage. Our system utilizes both the availability of metadata characteristics and the corpus of recorded history to uncover interesting associations in the characteristics space and generate recommendations, even in situations where the usage history is incomplete and the metadata characteristics are noisy and poorly named. Our results, obtained from both self-created testbeds and public benchmark datasets like OpenML, demonstrate the effectiveness of our proposed model in assisting data discovery by leveraging available data content and the analytical lifecycle in order to make automated intelligent suggestions by reflecting the expertise of the entire data community.
Computable Contracts by Extracting Obligation Logic Graphs

The emergence of contract specific programming languages has struggled to translate into widespread adoption of computable contracts due largely to high conversion costs. In this work, we present the first system for converting natural language contracts into code through the extraction of key entities, relationships, and formulas into a graph representation called the Obligation Logic Graph (OLG). This approach allows the semantic meaning of contract obligations, including dependencies between obligations, to be captured through the OLG and mapped to code downstream. We also introduce OLG extraction as a new joint entity and relation prediction task for legal contracts, and present the Contract-OLG dataset, consisting of 1,876 contract provisions, 18,597 entities and 18,170 relationships. We perform detailed experiments to understand the capabilities of state-of-the-art Transformer and graph-based models at completing these tasks, and identify where there is currently a significant gap between human expert and machine performance, particularly for relation extraction.
A Graph-Based Java Projects Representation for Antipatterns Detection

The identification and resolution of antipatterns in legacy Java code is a crucial process that can enhance the overall quality of software. However, detecting these antipatterns is often challenging, especially in large-scale projects where the codebase is complex and extensive. Relying solely on developers’ knowledge and expertise to identify antipatterns can be time-consuming, labor-intensive, and may result in missed opportunities to improve code quality. To address this challenge, this paper proposes a semi-automatic approach to antipattern detection in Java code-based systems. The approach involves the use of a graph-based representation of the software system, which captures the structural aspects of the code, such as classes, methods and their relations. The approach also employs mathematical rules to identify three common antipatterns: God Class, Circuitous Treasure Hunt, and Empty Semi-Truck. The proposed semi-automatic approach can support developers in identifying and resolving antipatterns that affect legacy code. By detecting these antipatterns, developers can improve software quality, maintainability, and reduce technical debt. Overall, this research offers a promising solution for detecting antipatterns in Java code through a semi-automatic approach, which can aid in the refactoring of legacy code.
Real-time Classifier of Multilingual Font Styles based on ResNet, SwordNet, Logistic Regression and Random Forest Algorithms

Different languages have different characters. At the same time, each character has a lot of font styles. This makes it difficult for humans to recognize different font styles for different characters. However, being able to detect and identify these font styles quickly and accurately has many important application use cases in different fields. At the same time, a large number of Internet users use web pages to query font styles. Therefore, I choose to make this real-time multilingual font style recognition algorithm. In this paper, I propose an algorithm that recognizes the input text and pictures in real time to judge the language and style of the text. It includes ResNet, SwordNet, logistic regression and random forest algorithms. The whole algorithm also calls pytesseract and Google Tesseract to realize text recognition and text positioning. I used Font Datasets used in "Font and Calligraphy Style Recognition Using Complex Wavelet Transform" for training. At the same time, I also built an image text recognition algorithm and generated various font styles as a data source. Based on this data, we adjusted the parameters and finally achieved an accuracy rate higher than 90%.
Using Probabilistic Context-Free Grammar to Create Password Guessing Models

This paper will discuss two versions of probabilistic contextfree grammar password-guessing models.The first model focuses on using English semantics to break down passwords and identify patterns.The second model identifies repeating chunks in passwords and uses this information to create possible passwords.Then, we will show the performance of each model on leaked password databases, and finally discuss the observations made on these tests.
Architecting Artificial Intelligence for Autonomous Cars: The OpenPilot Framework

Openpilot is a vast open-source semi-automated driving system developed by comma.ai, with 200+ contributors and 750K lines of code according to the OpenHub open-source community-tracking portal. On the one hand, the documentation available gives insights on what Openpilot is capable of doing, how to install it and how people can contribute to it, while the development team posts periodically update on the company’s blog on the state of the project and implementation of new features. On the other hand, this material does little in helping newcomers to embrace the technology with near-zero knowledge over its intended programming model. What is more, not having access to closed-source autonomous-driving framworks does not warrant an appropriate architectural analysis of the framework, the decisions to be made, and the concerns typically emerging in such a decision-making process. This study addresses this gap by exploiting mixed-methods research, featuring (a) an ethnographical study of the community’s operations and (b) a reverse engineering of the OpenPilot codebase both from a structure and operations perspective. This paper is intended as an early-stage adopter study for those software designers, operators, policy-makers and other practitioners aiming at embracing the aforementioned framework and programming model for their own professional endeavours.
Deep learning-based end-to-end 3D depth recovery from a single-frame fringe pattern with the MSUNet++ network

Deep learning (DL)-based single-frame fringe pattern to 3D depth reconstruction methods have aroused extensive research interest. The goal is to estimate high-precision 3D shape from a single frame of fringe pattern with limited information. Therefore, the purpose of this work attempts to propose an end-to-end DL-based 3D reconstruction method from the single fringe pattern with excellent capability of achieving high accuracy depth recovery and geometry details preservation of tested objects. We construct a multi-scale feature fusion convolutional neural network (CNN) called MSUNet++, which incorporates discrete wavelet transform (DWT) in data preprocessing for extracting high-frequency signals of fringe patterns as input of the network. Additionally, a loss function that combines structural similarity with edge perception is established. Through these measures, high-frequency geometry details of the reconstruction results can be obviously enhanced, while the geometric shape can be effectively maintained. Ablation experiments are involved in validating the effectiveness of our proposed solution. 3D reconstructed results and analysis of generalization experiments on different tested samples imply that the proposed method in this research enjoys capabilities of higher accuracy, better detail preservation, and robustness in comparison with the compared methods.
Method Strategies for Solving International Sports Intellectual Property Disputes through Chat GPT System

The Chat GPT system can provide valuable theoretical knowledge, legal basis, and relevant response strategies for resolving international sports intellectual property disputes. However, there are still incomplete explanations in terms of basic theories, professional knowledge, case inquiries, and even contradictory conclusions. The aim of this study is to make up for the shortcomings of the Chat GPT system in handling international sports intellectual property disputes through comparative analysis methods, establish the basic concepts that should be followed in international sports intellectual property disputes, clarify the professional theoretical knowledge in international sports intellectual property, point out the solution path for international sports intellectual property disputes, and ensure the fair resolution of international sports intellectual property disputes, And promote the healthy development of international sports industry.
Applying machine learning to model radon using topsoil geochemistry

Radon is classified as a Class 1 carcinogen, being the leading cause of lung cancer in non-smokers. Understanding the prominent sources of radon helps to mitigate against the adverse effects of radon exposure. Considering soil-gas radon is the main contributor to indoor radon, it is possible that soil geochemistry can be used as a proxy for the soil radon emanation potential or geogenic radon classes for a particular location. This paper investigates the relationship between soil geochemistry and geogenic radon. A large area of 17,983 km2 from the West, Midlands and East of Ireland was selected to represent a range of geology types and radon categories. A rigorous assessment is presented to investigate the relationship of geogenic radon and topsoil geochemistry; using univariate processes (i.e. r2, Pearson r and heatmaps) and multivariate techniques (i.e. principle component analysis (PCA) and machine learning (ML) algorithms including Gaussian process regression, logistic regression and random forest). Here, PCA and ML techniques were used to test the utility of soil geochemistry to predict geogenic radon classes. Gaussian Process Regression yielded the highest accuracy (74%) and f1-score (0.74) of all models. The feature importance (i.e. highest ranking elements for predicting geogenic radon class) from the ML models outputs elements including [Y, Tl, Mn, Cr, Co, Be, Sc and Rb]. The PCA biplot demonstrates that these elements cluster in conjunction with higher geogenic radon categories. Multivariate data analysis reveals that certain elements important for predicting higher geogenic radon classes, also covary together within topsoil samples; here these are termed “radon-prone elements”. Spatial covariance of radon-prone elements permits soil geochemistry to be used as a tool for understanding the distribution of geogenic radon. The methodology presented in this paper provides a comprehensive geo-statistical approach to investigate the relation between topsoil geochemistry and geogenic radon. This approach could be applied as a diagnostic tool to assist radon mitigation measures, hence adding value to legacy soil geochemistry datasets.
Using Agent-Based Simulations to Evaluate Bayesian Networks for Criminal Scenarios

Scenario-based Bayesian networks (BNs) have been proposed as a tool for the rational handling of evidence. The proper evaluation of existing methods requires access to a ground truth that can be used to test the quality and usefulness of a BN model of a crime. However, that would require a full probability distribution over all relevant variables used in the model, which is in practice not available. In this paper, we use an agent-based simulation as a proxy for the ground truth for the evaluation of BN models as tools for the rational handling of evidence. We use fictional crime scenarios as a background. First, we design manually constructed BNs using existing design methods in order to model example crime scenarios. Second, we build an agent-based simulation covering the scenarios of criminal and non-criminal behavior. Third, we algorithmically determine BNs using statistics collected experimentally from the agent-based simulation that represents the ground truth. Finally, we compare the manual, scenario-based BNs to the algorithmic BNs by comparing the posterior probability distribution over outcomes of the network to the ground-truth frequency distribution over those outcomes in the simulation, across all evidence valuations. We find that both manual BNs and algorithmic BNs perform similarly well: they are good reflections of the ground truth in most of the evidence valuations. Using ABMs as a ground truth can be a tool to investigate Bayesian Networks and their design methods, especially under circumstances that are implausible in real-life criminal cases, such as full probabilistic information.
Improving Translation of Case Descriptions into Logical Fact Formulas using LegalCaseNER

The automated translation of natural language text into structured logical representations is a critical task in various applications, including legal reasoning and decision-making. This paper presents a Name Entity Recognition (NER) based approach for translating the legal case descriptions written in natural language into PROLEG fact formulas. The approach comprises (1) extracting legal entities from the case description using a specialized NER model, namely LegalCaseNER and (2) transforming the extracted entities into PROLEG fact formulas using PROLEG rules. The experimental results demonstrate the efficacy of our proposed approach in accurately extracting relevant entities from legal case descriptions and translating them into the appropriate PROLEG fact formulas. Our approach provides a promising solution for handling complex and diverse case descriptions, enabling their representation in a structured format. This work provides a foundation for future research in the application of logical fact formulas in legal reasoning and decision-making.
Interactive Design of Museum Display System in the Context of New Media

With the continuous development and popularization of Internet technology, people's lifestyles have undergone earth-shaking changes. In today's society, people's demand for information is increasing, and traditional media forms can no longer meet the public's demand for information. For example, more and more users will access museum information and exhibit it through mobile phones or computers. At the same time, some merchants will take advantage of this convenience to conduct online exhibitions to attract more tourists to visit cultural relics and monuments or to post content about the collection on the Internet to spread cultural and educational purposes. This paper mainly focuses on the characteristics of new media, combined with the analysis of the current status of interaction design. The interaction design idea of a museum exhibit display system in the context of new media is proposed. Through the in-depth study of the characteristics of new media, combined with the functional requirements of the museum exhibit display system and the characteristics of the user group, the design principles and specific implementation methods of the interface of the museum exhibit display system are proposed.
Active Anomaly Detection for Autonomous Robots: A Benchmark

Autonomous robots require well-trained Anomaly Detection systems to detect unexpected hazardous events in unknown deployment scenarios. Such systems are difficult to train when data is scarce. During deployment, lots of data are collected but it is not apparent how to efficiently and effectively use that data. We propose to use Active Learning to select samples to improve the detection system performance. We benchmark 8 different query strategies, of which 2 are novel, using normalizing flow over image embeddings. While our results show that our approach has the best performance overall, choosing the right query strategy strongly depends on external factors.
A symmetric additive homomorphic encryption scheme based on NTRU proxy rekeys

To address the problems that homomorphic encryption cannot achieve secret sharing and existing Paillier cryptosystems cannot resist quantum attacks and are not suitable for scenarios where encrypted data is only uploaded by the data owner, this paper designs an NTRU (number theory research unit) based symmetric additive homomorphic encryption-proxy rekey (PAHE-PRK) scheme using the ideas of proxy rekeying and symmetric encryption based on the approximate convention number problem and the ring fault-tolerant learning problem. research unit) Proxy ReKey-based Symmetric Additive Homomorphic Encryption scheme (Partially Additive Homomorphic Encryption-Proxy ReKey, PAHE-PRK). The proxy can not only perform homomorphic computation on the original ciphertext, but also re-encrypt the homomorphic key so that the trusted user can obtain the homomorphic key to decrypt the ciphertext, thus achieving secret sharing and privacy protection. Finally, the performance and security of the proposed scheme are discussed in comparison with the traditional Paillier cryptosystem and the proxy re-encryption scheme based on the fault-tolerant learning problem, showing that the proposed scheme is faster in encryption and decryption, has less computation and storage overhead, and is resistant to the indistinguishability under chosen plaintext attack (IND-CPA).
Analysis of the Related Concepts of Green Skills Development from the Perspective of Vocational Education Modernization

Strengthening environmental protection and taking the road of green development are the relentless pursuit of the party and the people. The report of the 20th National Congress of the Communist Party of China "promote the great rejuvenation of the Chinese nation with Chinese-style modernization in an all-round way" opened a new era and a new journey mission and put "people and the modernization of the harmonious coexistence of nature" is one of the five characteristics of Chinese-style modernization. Green should become the proper meaning of vocational education. From the perspective of vocational education modernization, the article sorts out the four concepts of green economy, green industry, green occupation, and general skills that need to be clarified in the development of green skills and explains that green economy is the internal driving force of green skill development, and green industry is the driving force. Green skills development provides a new field, green occupation (job) is a new navigation mark for green skills development, and general skills provide essential content and direction for green skills development. Finally, the article presents the connotation and suggestions for green skills development in vocational education. It has important guiding significance for modernizing Chinese-style vocational education and empowering a beautiful China.