Reviewerly/exHarmony huggingface dataset:
Third-Party API Recommendation based on Heterogeneous Hypergraph Attention Networks

Third-party APIs (Application Programming Interfaces) are widely used in modern software development nowadays. Inspired by traditional recommender systems, recommending appropriate third-party APIs to developers has attracted a lot of research interest. Existing methods mainly focus on applying techniques such as Matrix Factorization (MF), Factorization Machine (FM), graph neural network (GNN) and hypergraph neural network (HGNN) to solve the recommendation problem. However, some limitations have not been well explored in existing methods: 1) MF and FM based API recommendation methods have difficulties in capturing the high-order interactions between users and APIs and are subject to noisy features. 2) GNN based methods can only be applied to simple graph structures, and suffer from the over-smoothing problem when aggregating high-order neighbor information. 3) HGNN based methods are focused on homogeneous hypergraphs and do not take the extra node attributes into consideration. To tackle the limitations, this paper proposes a third-party API recommendation method based on Heterogeneous Hypergraph Attention Network (HHAN). This method first constructs a heterogeneous hypergraph by exploiting the user-API interaction data and extra API attribute information. It then aggregates the neighbor information on the heterogeneous hypergraph to capture the high-order relationships between APIs and users. Finally, a node - and hyperedge-specific attention mechanism is designed to distinguish the importance of different types of neighbors. Extensive experiments on a real-world dataset crawled from ProgrammableWeb.com demonstrate the effectiveness of the proposed method.
Design and implementation of smart campus based on wechat mini program

With the rapid development of Internet technology in China, colleges and universities have launched a series of propaganda platforms in the form of new media, such as their official websites, in order to better publicize their schools and let more students know about them.However, in recent years, due to the popularity of smart phones and 5G technology, people's use of the Internet has shifted more from PC to smart phones, such as learning, communication, entertainment and access to news and information.In view of the above situation, universities and colleges have also established their own wechat public accounts in the early stage. However, due to the limited functions of wechat public accounts, most of the public accounts' introduction to the campus is limited to pictures and texts, which cannot show the characteristics of intelligent information in universities. In view of this situation, this paper takes universities as the research object and develops a smart campus system based on wechat mini program. Among them, the smart Campus wechat mini program server adopts MINA architecture, adopts the star network topology for development, and uses Bmob backend cloud to build the backend server of wechat mini program. The wechat mini program not only contains all kinds of information about the school, but also embedds a VR panoramic navigation map of the campus, realizing the comprehensive intelligence of campus information.
Noisy Data Cleaning Method Based on Adaptive Wavelet Threshold

Due to the ability of multi-resolution analysis, the wavelet-based method has become an effective tool in signal denoising, and has achieved good results, but shortcomings still remain. In view of the problem that the accuracy of traditional thresholds is greatly affected by sampling parameters, while the hard threshold function introduces discontinuous and soft one presents bias, this paper puts forward an adaptive wavelet threshold denoising method, comprising a sliding energy window based threshold processing method (SEWBT) and an iterative filtering based noise variance estimation method. Based on the mathematics mechanics of the wavelet transform for white noise, SEWBT is proposed to replace classical threshold techniques and thus immunes from the discontinuous and bias. Afterward, an iterative filtering based method for the noise variance estimation combining SEWBT and Lilliefors test is presented. The method is validated with simulated and online measured partial discharge signals. The results show that the proposal is insensitive to sampling parameters or noise variance. While SEWBT outperforms traditional threshold functions as it can eliminate the white noise effectively, and maintain the waveform of pulses favorably.
Developing Indonesian Medical Corpora Using the Latent Dirichlet Allocation Method and Filtering Out Non-Medical Terms and Non-Noun Words

As part of the research collaboration among BRIN, Solusi247, and the Harapan Kita Heart and Blood Vessel Hospital to develop an Indonesian medical speech recognition system that requires a medical text corpus, we have collected 300,063 medical Q&A articles (HTML files) about various diseases. This study is a preliminary step before developing the medical speech recognition system. As is known, a symptom of a disease can also be a symptom of another disease. For example, tiredness can be a flu or low blood pressure symptom. We intended to create medical corpora by clustering the medical documents according to the type of disease being conversed. The clustering was done using the Latent Dirichlet Allocation (LDA) method and the c_v measure. Before the clustering, we modified the articles by normalizing the medical terms and treating certain compound words as a single word. In the experiments, we used stop words containing non-medical terms and other stop words consisting of non-nouns. The experimental results showed that, on average, the c_v coherence score of the modified corpus is better than the original corpus, with a mean difference of 0.0122 and 0.0197, showing that the modification has a good impact.
Analysis and Prediction of Relative Humidity Level using Generalized Linear Model

The significance of humidity as a critical climate parameter impacts various sectors, including agriculture, health, and energy, necessitating a comprehensive understanding of its influencing factors. This study investigates the influence of climatic variables such as temperature, rainfall, sunshine duration, wind speed, and wind direction on the humidity levels in DKI Jakarta from 2019 to 2022. The objective is to develop a time-independent predictive model for humidity based on historical climate data. The methodology includes data pre-processing to impute missing values and replace outliers, followed by exploratory data analysis to ascertain variable distribution and inter-relationships. A regression model was initially employed for analysis, with subsequent application of regularization via a generalized linear model to enhance prediction accuracy. Results indicate that temperature, rainfall, sunshine duration, and wind direction significantly impact humidity levels in the investigated period. High inter-variable correlation posed challenges of multicollinearity and overfitting in the initial model. However, the application of regularization, trained with 75% of the historical dataset, mitigated these issues and improved model accuracy. This is evident in the improved Mean Squared Error (MSE) performance metrics of the Elastic-Net Regression Model (12.2), compared to the initial Multiple Regression Model (12.5). These findings hold potential implications for weather forecasting and climate change studies
A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs

In this work, we propose a spectral-based graph convolutional network for directed graphs. The proposed model employs the classic singular value decomposition (SVD) to perform signal decomposition directly on the asymmetric adjacency matrix. This strategy is simple, which allows many existing spectral-based methods to be adapted to directed graphs. We particularly utilize framelets-based filtering, which significantly enhances the learning capacity due to the separated modelling of information at different frequencies. We empirically observe the proposed model achieves state-of-the-art results on various datasets. We also show that the model is robust to feature perturbation.
Generalized column weights and equivalences of convolutional codes

We revisit the notion of [Formula: see text]th generalized column weights for the [Formula: see text]-truncation of a convolutional code. Taking the limit as [Formula: see text] tends to infinity, we define [Formula: see text]th generalized column weights of a convolutional code. We introduce suitable notions of [Formula: see text]-equivalence and equivalence, with respect to which generalized column weights are code invariants. We establish some properties of these invariants and compare them with other definitions of generalized distance and weight which appear in the literature.
NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links

This paper describes NEREL—a Russian news dataset suited for three tasks: nested named entity recognition, relation extraction, and entity linking. Compared to flat entities, nested named entities provide a richer and more complete annotation while also increasing the coverage of relations annotation and entity linking. Relations between nested named entities may cross entity boundaries to connect to shorter entities nested within longer ones, which makes it harder to detect such relations. NEREL is currently the largest Russian dataset annotated with entities and relations: it comprises 29 named entity types and 49 relation types. At the time of writing, the dataset contains 56 K named entities and 39 K relations annotated in 933 person-oriented news articles. NEREL is annotated with relations at three levels: (1) within nested named entities, (2) within sentences, and (3) with relations crossing sentence boundaries. We provide benchmark evaluation of current state-of-the-art methods in all three tasks. The dataset is freely available at https://github.com/nerel-ds/NEREL .
EDUCATIONAL REFORMS - WAYS TO ENSURE QUALITY

In this article, studies were made on reforms in the education system, the ratio of students and teachers, education costs, and directions for improving the quality of education, and relevant conclusions and suggestions were made. At the same time, some comments on the influence of the correct distribution of study and work time of students and professors on the quality of education have been formed.
Analysis of Minimizing Iron Material Waste for Construction Work in Wetlands with Bar Bending Schedule Method

The components of the material structure that are important in calculating requirements are iron requirements because iron requirements have a high value in construction work. Iron requirements such as reinforcing columns, beams, pile caps, sloofs, and others are widely used for reinforcement. The research aims to determine the need for stirrup reinforcement using a method based on field data with the Bar Bending Schedule (BBS) approach. The BBS method minimises the waste of material so that it only leaves the material in the screw-type iron reinforcement, around 14.79% for iron reinforcement with a diameter of 10 mm and 6.65% for iron reinforcement with a diameter of 16 mm. As well as for plain iron reinforcement, only leave material around 0.01% on mm diameter iron reinforcement. The bar bending schedule method can optimize the remaining material from the remaining length of the iron by reusing it for other iron piece plans where the piece’s dimensions can meet the previous rest.
Integrated Management Technology of Geophysics and Geology Data

This paper describes the three main development stages of geophysics and geology data management, analyzes the business and data characteristics faced at each stage, discusses the development trend of integrated geophysical and geological data management with the development of information technology and database technology; analyzes the challenges faced by geophysical and geological data in terms of data collection, data storage and data application, explains the sources and causes of the problems. The concept of integrated management of geophysical and geological data is proposed, based on the data governance framework system, the integrated data management platform is built, covering the construction of data source collection, efficient data services and comprehensive data management, so as to realize unified, efficient and secure data collection, management and application services, the content and characteristics of the main data management strategy of geophysical and geological data are analyzed, and on the basis of the above discussion. Finally, it explores and practices the application of this technology system outside the oil and gas field, proposes the application support scheme of integrated geophysical and geological data management for business, and further demonstrates the value and significance of the wide business application of integrated data management.
Investigation and Research on Group Cooperative Learning in Junior High School Mathematics Teaching

Mathematics classroom under the concept of Curriculum Standard is one of the effective ways to cultivate students' autonomy, cooperation and inquiry. Cooperative learning is used to improve the efficiency of mathematics classroom. However, there are still some problems in the process of using it. One of the manifestations of some students in many schools is that they don't actively participate in group discussions in class, and the learning effect is obviously not high. Therefore, students in the mathematics classroom are analyzing the learning problems involved in the junior high school mathematics classroom at this stage, aiming at the existing effective methods and strategies, combining the characteristics of junior high school mathematics, and can use them to improve the efficiency of practical classroom teaching practice, so that group collaborative learning and junior high school mathematics classroom can be effectively integrated.
A study on the measurement and standardized assessment model of student learning outcomes in vocational institutions

Abstract As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.
Cloud Computing for Effective Cyber Security Attack Detection in Smart Cities

An astute metropolis is an urbanized region that accumulates data through diverse numerical and experiential understanding. Cloud-connected Internet of Things (IoT) solutions have the potential to aid intelligent cities in collecting data from inhabitants, devices, residences, and alternative origins. The monitoring and administration of carrying systems, plug-in services, reserve managing, H2O resource schemes, excess managing, illegal finding, safety actions, ability, numeral collection, healthcare abilities, and extra openings all make use of the processing and analysis of this data. This study aims to improve the security of smart cities by detecting attacks using algorithms drawn from the UNSW-NB15 and CICIDS2017 datasets and to create advanced strategies for identifying and justifying cyber threats in the context of smart cities by leveraging real-world network traffic data from UNSW-NB15 and labelled attack actions from CICIDS2017. The research aims to underwrite the development of more effective intrusion detection systems tailored to the unique problems of safeguarding networked urban environments, hence improving the flexibility and safety of smart cities by estimating these datasets.
ETMS: Efficient Traffic Management System for Congestion Detection and Alert using HAAR Cascade

Rapid social development has resulted in the emergence of a new major societal issue: urban traffic congestion, which many cities must address. In addition to making it more difficult for people to get around town, traffic jams are a major source of the city's pollution crisis. In order to address the problems of automobile exhaust pollution and congestion, this paper uses the system dynamics approach to develop a model to study the urban traffic congestion system from the perspectives of trucks,private cars, bikes and public transportation. This project proposes a system for detecting vehicles and sending alerts when traffic levels rise to dangerous levels using Haar Cascade and Fuzzy Cognitive Maps (FCP). The proposed system uses Haar Cascade to detect moving vehicles, which are then classified using FCP. The system can make decisions based on partial or ambiguous information by utilising FCP, a soft computing technique, which allows it to learn from past actions. An algorithm for estimating traffic density is also used by the system to pinpoint active areas. In congested areas, the system will alert the driver if it anticipates a collision with another vehicle and also Experiments show that the proposed system is able to accurately detect vehicles and provide timely alerts to the driver, drastically lowering the probability of accidents occurring in heavily travelled areas.&#x0D; The importance of introducing such a system cannot be overstated in today's transportation system. It's a big deal for the future of intelligent urban planning and traffic control. Congestion relief, cleaner air, and increased security are just some of the long-term benefits that justify the high initial investment. To add, this system is adaptable to suburban and rural areas, which can also experience traffic congestion issues.
Malicious URL Website Detection using Selective Hyper Feature Link Stability based on Soft-Max Deep Featured Convolution Neural Network

The web resource contains many domains with different users' Uniform Resource Locators (URLs). Due to the increasing amount of information on the Internet resource, malicious activities are done by hackers by expecting malicious websites in URL sub-links. Increasing information theft leads data sources to be vested in huge mediums. So, to analyze the web features to find the malicious webpage based on the deep learning approach, we propose a Selective Hyper Feature Link stability rate (SHFLSR) based on Soft-max Deep featured convolution neural network (SmDFCNN) for identifying the malicious website detection depends on the actions performed and its feature responses. Initially, the URL Signature Frame rate (USFR) is estimated to verify the domain-specific hosting. Then the link stability was confirmed by post-response rate using HyperLink stability post-response state (LSPRS). Depending upon the Spectral successive Domain propagation rate (S2DPR), the features were selected and trained with a deep neural classifier with a logically defined Softmax- Logical activator (SmLA) using Deep featured Convolution neural network (DFCNN). The proposed system performs a high-performance rate by detecting the malicious URL based on the behavioral response of the domain. It increases the detection rate, prediction rate, and classifier performance.
Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks

In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency.
While Loops in Coq

While loops are present in virtually all imperative programming languages. They are important both for practical reasons (performing a number of iterations not known in advance) and theoretical reasons (achieving Turing completeness). In this paper we propose an approach for incorporating while loops in an imperative language shallowly embedded in the Coq proof assistant. The main difficulty is that proving the termination of while loops is nontrivial, or impossible in the case of non-termination, whereas Coq only accepts programs endowed with termination proofs. Our solution is based on a new, general method for defining possibly non-terminating recursive functions in Coq. We illustrate the approach by proving termination and partial correctness of a program on linked lists.
A Parallel Dynamic Epistemic Perspective over Muddy Children Puzzle

Epistemic protocols represents a current field of interest, with numerous approaches still being studied. In this paper we formalize parallel sessions of the The Muddy Children Puzzle using Public Observation Logic, a system that allows epistemic update. We consider agents with roles in multiple sessions and the information update in all parallel sessions as new information is discovered in any particular session.