Reviewerly/exHarmony huggingface dataset:
Construction of Intelligent Textbook Courseware Management System Based on Artificial Intelligence Technology

On the one hand, the lack of specialized knowledge and knowledge of software development is serious. Second, the development process takes a lot of time, and the development cost is high. Shortening the development time, reducing the development cost, and reducing the dependence on artificial intelligence can be realized by developing an intelligent courseware writing system. The research of intelligent teaching system based on Web and multiagent technology in this paper will surely promote the continuous development and progress of intelligent teaching and related disciplines, which has profound theoretical and practical significance. The design of the model may need to be emphasized and improved in the understanding and reform of classroom management concepts. The comprehensive improvement has an increase of 86.3%, which is of great significance for courseware management. In terms of courseware management, due to the model design, it is also well reflected in personalization, and the improvement of management is 74.6%.
Wikinomics in the higher education: the need to use tools and instruments

The article is devoted to the possibility of using the latest management models in modern higher education. A new economic model based on the use of information technology is analyzed - this model is called "Wikinomics".&#x0D; Herein the essence of this model is determined - namely, peering, i.e. cooperation of equals, as the basis for the existence of the whole wikinomistic paradigm. The following key principles of wikinomics: openness, exchange of ideas, equality and globality are studied, as well as their content is adapted for use in the provision of higher education services.&#x0D; In the article the possibility of using the tools of modern management models in higher education, namely wikinomics, is analyzed. The mechanism of functioning of online platforms of the educational courses is considered, and their compliance with the existing requirements of higher education is determined. The possibility of standardizing the use of wikinomistic approaches in higher education through non-formal and informal education is studied. The essence of non-formal and informal education is considered and the thesis that wikinomics is a manifestation of informal education is proved. The importance of adhering to the wikinomistic principles in informal education is proved. The need for the existence of online educational platforms as a tool for building an individual educational trajectory is postulated.&#x0D; It is argued, that educational courses that are located on the recognized online platforms can and should be considered as the full-fledged educational components of the educational programs.&#x0D; Herein the necessity of using the principles and approaches of wikinomics in higher education as the guarantee of rapid response to the changing labor market requirements and as the effective tool for increasing the competitiveness of modern students is substantiated
CoRGi: Content-Rich Graph Neural Networks with Attention

Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value imputation, items --- represented as nodes --- may contain rich textual information. However, when processing graphs with graph neural networks (GNN), such information is either ignored or summarized into a single vector representation used to initialize the GNN. Towards addressing this, we present CoRGi, a GNN that considers the rich data within nodes in the context of their neighbors. This is achieved by endowing CoRGi's message passing with a personalized attention mechanism over the content of each node. This way, CoRGi assigns user-item-specific attention scores with respect to the words that appear in an item's content. We evaluate CoRGi on two edge-value prediction tasks and show that CoRGi is better at making edge-value predictions over existing methods, especially on sparse regions of the graph.
Falling and Drowning Detection Framework Using Smartphone Sensors

Advancements in health monitoring using smartphone sensor technologies have made it possible to quantify the functional performance and deviations in an individual's routine. Falling and drowning are significant unnatural causes of silent accidental deaths, which require an ambient approach to be detected. This paper presents the novel ambient assistive framework Falling and Drowning Detection (FaDD) for falling and drowning detection. FaDD perceives input from smartphone sensors, such as accelerometer, gyroscope, magnetometer, and GPS, that provide accurate readings of the movement of an individual's body. FaDD hierarchically recognizes the falling and drowning actions by applying the machine learning model. The approach activates embedding, in a smartphone application, to notify emergency alerts to various stakeholders (i.e., guardian, rescue, and close circle community) about drowning of an individual. FaDD detects falling, drowning, and routine actions with good accuracy of 98%. Furthermore, the FaDD framework enhances coordination to provide more efficient and reliable healthcare services to people.
COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation

Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their click-through-rate (CTR) and quality. However, evolving ad formats and changing creative requirements make it difficult to generate optimized & customized headlines. We propose a novel method that uses prefix control tokens along with BART [16] fine-tuning. It yields the highest CTR and also allows users to control the length of generated headlines for use across different ad formats. The method is also flexible and can easily be adapted to other architectures, creative requirements and optimization criteria. Our experiments demonstrate a 25.82% increment in Rouge-L and a 5.82% increment in estimated CTR over previously published strong ad headline generation baseline.
Regional-Local Adversarially Learned One-Class Classifier Anomalous Sound Detection in Global Long-Term Space

Anomalous sound detection (ASD) is one of the most significant tasks of mechanical equipment monitoring and maintaining in complex industrial systems. In practice, it is vital to efficiently identify abnormal status of the working mechanical system, which can further facilitate the failure troubleshooting. In this paper, we propose a multi-pattern adversarial learning one-class classification framework, which allows us to use both the generator and the discriminator of an adversarial model for efficient ASD. The core idea is to learn reconstructing the normal patterns of acoustic data through two different patterns from auto-encoding generators, which succeeds in generalizing the fundamental role of a discriminator from identifying real and fake data to distinguishing between regional and local pattern reconstructions. Moreover, we design a novel balanceable detection strategy using both generators and a discriminator to achieve anomaly detection efficiently. Furthermore, we present a global filter layer for long-term interactions in the frequency domain space, which directly learns from the original data without introducing any human priors. Extensive experiments are performed on four real-world datasets from different industrial domains (three cavitation datasets from SAMSON AG, and one existing publicly) for anomaly detection, all showing superior results and outperform recent state-of-the-art ASD methods.
Effective Graph Mining for Educational Data Mining and Interest Recommendation

In order to fully understand and analyze the rules and cognitive characteristics of users' learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users' learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users' resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.
Artificial Intelligence System Detection in English Teaching Based on Heuristic Genetic Algorithm

Using the traditional English teaching mode is difficult to help correct students, and it is difficult to achieve human-computer interaction in oral English communication. In order to improve the effect of English detection and improve teaching efficiency, this article builds an artificial intelligence-assisted teaching system suitable for English teaching based on heuristic genetic algorithms. Furthermore, this article extends the multioffspring genetic algorithm, improves the offspring generation method, and proposes GMOGA, which makes the choice of the number of offspring more flexible. At the same time, it also enables the value of the number of children of the algorithm to be a value that cannot be obtained by the previous algorithm, which further improves the efficiency of the algorithm. In addition, this article combines the actual needs to construct the functional structure of the artificial intelligence system and designs two sets of comparative experiments to verify and analyze the model's performance. The research results show that the model constructed in this article meets the multifunctional requirements of the system and can be applied to practice.
Educational Data Mining by Optimally Fusing Shallow and Deep Features

In order to meet the requirement of designing the educational curriculum and extract the relevant data toward students, we in this paper utilize the deep learning architecture to study the personalized understanding and educational feature extraction algorithm of the high school and college students. Aiming at the insufficiency of deep collaborative filtering recommendation algorithm that can only discover the paths that have attracted students’ attention, the requirements of intelligent course are proposed by us. First, the word vector permutation discourse poke model is extracted to produce audible vectors of message data, a well-designed learning algorithm is proposed subsequently to extract and fuse multiple heterogeneous educational data. Then, the SVD algorithm program is used to reduce the range of feature vectors. And thus, the model is completely functional and we identify unified channels and users. The method of combining hand-extracted low-level features with high-level human gesture features (image visual gate features and image deep features) automatically extracted by our adopted deep network is used to identify students’ various emotions. The conclusive product is a flat semantic description that effectively detects copying and objection. Experiments are performed on a generic dataset, and we use the old-fashioned manual birth method and two well-known data sets VGG16 and fine-tune AlexNet.
3D Reconstruction Method based on Medical Image Feature Point Matching

Medical 3D image reconstruction is an important image processing step in medical image analysis. How to speed up the speed while improving the accuracy in 3D reconstruction is an important issue. To solve this problem, this paper proposes a 3D reconstruction method based on image feature point matching. By improving SIFT, the initial matching of feature points is realized by using the neighborhood voting method, and then the initial matching points are optimized by the improved RANSAC algorithm, and a new SFM reconstruction method is obtained. The experimental results show that the feature matching rate of this algorithm on Fountain data is 95.42% and the matching speed is 4.751 s. It can be seen that this algorithm can shorten the reconstruction time and obtain sparse point clouds with more reasonable distribution and better reconstruction effect.
Explicit construction of the square-root Vélu's formula on Edwards curves

The square-root Vélu's formula ($\elu$'s formula) is known to be effective way to speed up the computations of higher-degree isogeny used for isogeny-based cryptosystems such as CSIDH and B-SIDH. The original formula was proposed using Montgomery curves, and Moriya et al. then extended it to Edwards curves without specific construction method. In this study, we explicitly show how to construct $\elu$'s formula on Edwards curves. In particular, we provide a method for adjusting for the differences between two resultants. We also compare the computational complexity with the original $\elu$'s formula and find that it is up to 8\% faster.
Research on the Cultivation of College English Listening, Speaking, Reading, and Writing Ability by VR Technology

With the development of VR technology, it is possible to apply innovation to English teaching, and realize new breakthroughs and innovations in English teaching. In the background of information technology widely used in teaching, VR technology has become a very popular new technology because it can promote teaching better and make teaching effect more obvious; Through VR technology in English learning in listening, speaking, reading, and writing mode, we summarize the application of this technology in education industry at home and abroad, and put forward the purpose and significance of this research. Firstly, through the listening, speaking, reading, and writing mode of VR technology in English learning; In order to better evaluate the performance comparison under different modes, the score and weight distribution method are carried out for listening, speaking, reading, and writing. Secondly, by evaluating the value of E(X) in the nonexisting combination mode, the corresponding optimal proportion distribution scheme can be obtained. Finally, by comparing the scores of 8 groups of people under VR technology and statistical learning mode, the results show that VR technology has obvious advantages in improving listening, speaking, reading, and writing scores.
Improving the Learning and Teaching of Mathematical Logic Elements Using Maple

The study of the elements of mathematical logic and its sections, especially in the process of teaching in high school in general and specialized courses of mathematics or higher educational institutions, is a rather tricky task. The paper demonstrates how one can design and develop a package of interactive applications to help learn, understand, and apply the essential elements of mathematical logic. The research aims to develop and describe an interactive application for studying the order of constructing truth tables and studying and demonstrating the production of principal normal disjunctive and conjunctive forms. The development is based on the methods of the Maple computer algebra system of the Maplet library. In addition, other methods are used to solve the research issues – theoretical and accurate analytical solutions to mathematical logic issues. All constructed material (Maplet) will serve as a good simulator for students, allowing one to perform an infinite number of attempts to analyze, synthesize, and build truth tables.
Ensemble Learning-Based Multi-Cues Fusion Object Tracking in Complex Surveillance Environment

The vast majority of currently available kernelized correlation filter (KCF)-based trackers simply make use of a single object feature to define the object of interest. It is impossible to avoid tracking instability while working with a wide variety of complex videos. In this piece of research, an ensemble learning-based multi-cues fusion object tracking method is offered as a potential solution to the issue at hand. Using ensemble learning to train multiple kernelized correlation filters with different features in order to obtain the optimal tracking parameters is the primary concept behind the improved KCF-based tracking algorithm. After that, the peak side lobe ratio and the response consistency of two adjacent frames are used to obtain the fusion weight. In addition, an adaptive weighted fusion technique is applied in order to combine the response findings in order to finish the location estimation; finally, the tracking confidence is applied in order to update the tracking model in order to prevent model deterioration. In order to increase the adaptability of the revised algorithm to size-change, a Bayesian estimate model based on scale pyramid has been presented. This model is able to determine the optimal scale of the object, which is the goal of this endeavor. The tracking results of a number of different benchmark movies demonstrate that the algorithm that we have suggested is able to effectively eliminate the effects of interference elements, and that its overall performance is superior to that of the comparison algorithms.
Study on A face detection method based on elliptic skin color model

Human face is a natural structural target with abundant details, and its detection results are easily affected by facial details, expressions and posture changes. In color images, the distribution of skin color is not affected by the changes of facial details, expression and posture, and the speed of skin color detection is very fast. This paper proposes a face detection algorithm based on elliptic skin color model. In YCbCr color space, the elliptic skin color model and logistic regression analysis were used to determine the skin color probability of each point, and the pixels of each point were mapped to [0, 1]. Based on Ostu method, a parallel genetic algorithm was used to determine the threshold of skin color segmentation to segment the face region. The results show that this method improves the speed of face detection and has good robustness to posture and expression changes.
Channel-Robust Specific Emitter Identification Based on Transformer

Specific emitter identification (SEI) refers to the process of identifying emitter individuals based on corresponding wireless signals. Although deep learning has been successfully applied in SEI, the performance remains to be improved when the channel changes. In this paper, we suggest that a potential reason of the performance degradation is inadequacy of model capacity. Therefore, Transformer, an advanced neural network architecture with large model capacity, is applied for channel-robust SEI. Experimental results show that Transformer achieves better performance than conventional convolutional neural networks.
The Distinction of Logical Decision According to the Model of the Analysis of Brain Signals (EEG)

Recently, brain signal patterns have been recruited by researchers in different life activities.Researchers have studied each life activity and how brain signal patterns appear.These patterns could then be generalised and used in different disciplines.In this paper, we study the brain state during decision making in a lottery experiment.An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making.After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.
Potensi Energi Angin pada Sisi Siku Atap Gedung Tinggi

Wind is moving air caused by the earth's rotation and also because of the difference in air pressure around it. The wind flows that occur include laminar flow and turbulent flow. Judging from the effect of spreading its direction, the wind that passes through a building, its flow will split into horizontal and vertical axes and pass along the geometry and structure of the building. This happens because the wind speed increases due to the increased wind pressure along the side of the building. Departing from this hypothesis, this research was conducted to compare the velocity potential on the angle side of the building roof with the speed at the top of the building (Rooftop) as a starting point for the development of new renewable energy engineering, especially wind energy. This experiment was conducted at the Dean Building of the Faculty of Engineering, Sultan Ageng Tirtayasa University, Cilegon, Banten. After conducting experiments with reference to the International Electrotechnical Commission (IEC) 61400-12-1 standard, this research found that on the angle side of the building the average and maximum velocity are higher than those on the roof of the building, also the wind energy potential is higher, amounting to ≥20.94%. In terms of Daily Wind Energy Potential, the elbow side of the building is proven to be more efficient with an increase in efficiency of 42.2%.
Language Literacy Enhancement Through Zoom Cloud Meeting

This research conducted to analyze the language literacy skills enhancement through zoom cloud meeting. From the research problem, the researchers formulated the purpose of this study was to describe language literacy skills through zoom cloud meetings at Universitas Prima Indonesia in 5th semester and explained the parts of language literacy skills dominantly increased during online learning through zoom cloud meeting in 5th semester students at Universitas Prima Indonesia. This research used qualitative research with the case study method where in data collection using interview by sharing the google form, distributed to 30, 5th semester students at Universitas Prima Indonesia for the Academic Year 2021/2022. The results found that the use of zoom during online learning really helped students in improving their language literacy skills and language literacy skills for Indonesian prima university students when using zoom cloud meetings in online learning, 66.7% and above said it was very good and the literacy section. The dominant language literacy skill enhanced was using zoom cloud meetings is reading ability with a percentage of 53.3%, then followed by listening ability at 26.7%, speaking ability at 16.7%, and writing ability with a percentage of 3.3%
An Ontological Exploration of Central Bank Digital Currency Governance Design

Financial ecosystems and their related transactions are increasingly relying on big tech payment service providers, such as ApplePay and WeChat. By offering these services, transacting in unregulated cryptocurrencies becomes easier. Consequently, big tech companies take a powerful position in the ecosystem, such dominance may be avoided by a decentralized ecosystem, in which decision making power is distributed over several actors. Emergence of several highly unregulated cryptocurrencies and increased reliance on big tech, motivates central banks to investigate alternatives, called Central Bank Digital Currency (CBDC) that can be subject to governance and rules. CBDC is specifically aimed to decrease dependency on largely uncontrolled big tech payment service providers and to limit the growth of unregulated cryptocurrencies. In this paper, we explore the key question of how to design a governance structure, we do that by applying the DECENT ontology and conceptual models to the real world use-case of CBDC.