Reviewerly/exHarmony huggingface dataset:
A Bibliometrics Study: Enhancing Management Education Using Blockchain Technology

In the globalization era, a nation’s progress is primarily determined by science and technology, where the technology currently developing is entering the digital age. Technology is not only used daily but also does not flee the latest technological innovations, which is Education. Technology and Education are instrumental in managing organizational Management to be more directed, controlled, and orderly. This study centers around blockchain Enhancing Management in Education. Given these examinations, articles that investigate blockchain applications have been checked, broken down, and organized to absorb a proper data set curation. A two-step way to distinguish the articles had to be examined and explored to achieve this study’s motivations. In the initial step, Data characterized relevant pursuit terms for digging the Scopus data set for significant articles. This data set was decided because of its extensive inclusion of companion audited research in legitimate diaries and its unavoidable presence in the local scholastic area. In the subsequent step, we utilized explicit rules to decide the consideration of articles in our data set for the bibliometric and content examinations. The study’s novelty in using blockchain technology to improve management education is in the early stages of discussion and application development. As the bibliometric analysis has shown, the analysis shows that the impact of its use has increased. Education, enriching intellectual property to expand and improve systems/applications used to improve reputation and overcome problems, especially the Management.
Increasing Student Satisfaction Learning Outcomes by using the Mobile Learning Platform

Constraints that are often faced by students are too easy to feel bored in learning. Therefore, interest in learning becomes less. Stakeholders must address these problems so that students can use information technology in learning, especially in the development of science and technology in the era of the industrial revolution 4.0. After working closely with a group of academics, a university developed a mobile learning platform using an LMS called Digital Business Course, which allows students to quickly access various learning resources by downloading an app to their smartphone. This study aims to see the results of the platform improving the analytical capabilities of Business Intelligence as a Business Aid. In this study, 230 students were randomly selected. This research contributes to improve multivariate quantitative analysis and qualitative interview data analysis. The conclusions obtained are as follows: (1) Participants who use the mobile learning platform are more satisfied than participants who do not use it in the BA class. (2) In BA class, the learning outcomes of participants who use mobile learning platforms increase significantly compared to those who do not. (3) In the BA class, the cognitive load of participants who use a mobile learning platform is significantly lower than that of participants who do not use a mobile learning platform. The novelty of this research is that the Digital Business mobile learning platform is superior, gets higher learning outcomes and carries less cognitive load.
Convolutional Neural Networks and Bidirectional Gated Recurrent Unit Model Based Photovoltaic Power Prediction

With the rapid expansion of renewable energy, more and more attention is being focused on predicting the solar photovoltaic field. Efficient and accurate prediction of photovoltaic power generation is crucial for the stable operation of the power system. In this paper, a model based on a convolutional neural network and the bidirectional gated cyclic unit is proposed, denoted as the CNN-BiGRU mode, for ultra-short-term photovoltaic power prediction. Firstly, the Pearson correlation coefficient is used to analyze the correlation among PV output power and meteorological factors, and the total irradiance and scattering irradiance are selected as the model's input variables. Secondly, a convolutional neural network (CNN) is used to mine potential connections among data, and a bidirectional gated loop unit (BiGRU) is used to capture temporal dependencies among data. Finally, meteorological factors and corresponding historical data of PV power are selected, and then the output power of the PV power station is predicted by the CNN-BiGRU algorithm. Example analysis shows that the model can reduce the data dimension and improves the prediction accuracy.
ASSESSING THE EFFECTIVENESS OF DEFENSES AGAINST ADVERSARIAL ATTACKS IN NEURAL NETWORKS

Neural Networks consisting of a hierarchy of many layers have been applied to accomplish tasks in Computer Vision such as Image Recognition, Face Detection etc.However, Deep Neural Networks, especially Convolutional Neural Networks are not yet robust to inputs containing adversarial inputs.The performance of CNNs drops sharply and unpredictably when subjected to adversarial inputs.Adversarial inputs are artificially synthesized inputs that are similar to the authentic input dataset in appearance to the human eye but yield vastly unpredictable results when fed to a neural network.To devise an effective robustness mechanism against neural networks, all the factors that influence the confidence and probability of a certain output, need to be studied.In this paper, the authors have studied and analyzed the adversarial attacks and available defense mechanisms by using the ART library launched by IBM.Through the study, two defense methods emerged as the best-performing methods.
Who Cares When Value (Mis)Reporting May Be Found Out? An Acquiring-a-Company Experiment with Value Messages and Information Leaks

We modify the Acquiring-a-Company game to study lying in ultimatum bargaining. Privately informed sellers send messages about the alleged value of their company to potential buyers. Via random information leaks, buyers can learn the true value before proposing a price which the seller finally accepts or not. Two-thirds of all sellers exaggerate the company’s value to persuade buyers to offer more, especially when the true value is small. Surprisingly, a higher leak probability does not increase truthtelling. However, it decreases overreporting and increases underreporting. Buyers who found out value misreporting anchor their price proposals on the true value but do not explicitly discriminate against liars. Sellers are fully opportunistic and make their acceptances dependent on the resulting positive payoff. Even if morality concerns do not seem to matter much, probabilistic leaks enhance welfare. That suggests to politically facilitate and encourage e.g. whistle blowing.
Sistem Informasi Penjualan Online Pada Toko Gandaria Jambi

Gandaria Jambi shop is a shop engaged in the sale of plastic and cake ingredients, in this Gandaria Jambi shop the obstacles experienced are a decrease in sales turnover due to a conventional sales system, customers have to come directly to the store to purchase products and promote products only by word of mouth. Then an Online Sales Information System was designed at the Gandaria Jambi Store with the aim of helping increase sales at the Gandaria Jambi Store and making it easier to market products. In this study using prototype modeling and it is hoped that the results of the research will make it easier for customers to make purchases, order and confirm payments and for the store to have an online sales plan that can assist in sales transactions to be more effective in doing business. In the future, the analysis and design of this online sales information system can be developed such as the feature of submitting and returning goods from shoppers at Gandaria Jambi Shop
Neural network and system for attitude and behavior detection based on pressure data

In the process of monitoring the behavior of the elderly, wearable devices and visual devices are easily limited by the site and environment, resulting in poor monitoring results.This paper proposes a posture behavior detection method and system based on pressure data.The convolutional neural network algorithm is used to identify the pressure data to detect the posture, calculate the posture holding time and posture change frequency, judge the posture change action process according to the trajectory of the pressure center point, and finally record and analyze the user's behavior.The correct rate of pose classification of the model used in this paper has reached 98.69%, and the correct rate of pose retention time has reached 98.06%.Finally completed the research and development of the relevant monitoring system, which can be used in the field of medical treatment and daily care.
On the learning machine in quaternionic domain and its application

There are various high-dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. In various literatures, the conventional real-valued neural networks are tried to solve the problem associated with high-dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalisation capability of the proposed learning machine is presented through 3D linear transformations, 3D face recognition and chaotic time series predictions (Lorenz system and Chua's circuit) as benchmark problems, which demonstrate the significance of the work.
An Adaptive Parameter DBSCAN Clustering and Reputation-Aware QoS Prediction Method

QoS prediction for Web services is a hot research topic in service computing, driving the development of service recommendation techniques. Collaborative filtering algorithms have been widely used in QoS prediction. As a group intelligence algorithm, it relies on a large amount of historical QoS data, so the data credibility should be ensured first. Previous work, attempts to identify outliers through clustering algorithms, however, historical QoS data is too sparse and contains a large number of services with varying data distributions, It is difficult to determine the number of clusters that apply to all services, so classification errors often occur. To solve the above problems, in the method proposed in this paper, the RSVD model considering user and service deviation is firstly used to fill the matrix to alleviate the impact of data sparsity, and the Adagrad algorithm is used to optimize the accuracy. Then the outliers are detected in two stages. In the first stage, the DBSCAN clustering algorithm with adaptive parameters is applied, according to the data distribution of each service, suitable parameters are matched for clustering, the number of clusters and a single outlier were mined by themselves, and similar users are obtained. The second stage removes common sense outliers. Count the number of outliers provided by users in both phases to get untrustworthy users. Finally, QoS values are predicted based on the trusted data. The experimental evaluation results show that compared with other baseline methods, the method in this paper has a great improvement in prediction accuracy.
Correction to: Docs for Developers

The original version of the book was published with the author name Zachary Sarah Corleissen, not mentioned appropriately and it has now been updated everywhere in the book as Sarah Corleissen.
FACIAL EXPRESSION USING NETWORK OF NEURONS

It is really exciting to comprehend human gestures for widespread lifestyle applications.Facial expressions play a vital role in social interactions by giving complete information regarding the emotional condition of the person.It is not an easy task to identify emotions as there is a lot of complexity and variability in everyone's emotions, like are five basic emotions namely, happiness, sadness, anger, surprise, and fear.In this paper, a study for Expression of the basic emotions is carried out using the Viola-Jones algorithm and neural network.
Research on the ability of undergraduate nursing health assessment under the mixed teaching mode

Objective: To study the ability of undergraduate nursing health assessment under the mixed teaching mode.Methods: The health assessment ability of the test group and the control group was analyzed by the analytic hierarchy process.Then add the test of t value and p value to make a significant analysis of the health assessment ability of the test group and the control group.The mixed teaching mode and the traditional teaching mode were used to teach the health assessment ability of the experimental group and the control group respectively.Results: The overall evaluation of the health assessment ability of the experimental group was higher, and the quality of nursing was improved.Conclusion: The use of mixed teaching mode can effectively improve the quality of nursing teaching and plays an important role in the improvement of nursing quality.
Research on the educational reform of academic English writing course from the perspective of diversification

The conventional academic English writing course education and teaching mainly follows the original fixed teaching mode.In the diversified perspective, the teaching resources are rapidly expanded.In order to meet the current academic English writing teaching needs, it is necessary to carry out in-depth research on
Application of Online Course Resources in College English Teaching

The introduction of online courses such as microlectures and MOOCs has profoundly affected the education ecology.The use of online courses and related resources can promote the improvement of English teaching quality and help students to carry out independent and personalized learning, so that the classroom instruction efficiency can be enhanced.This paper analyzes the importance of online course resources, sorts out the problems related to online course resources in the current English teaching in colleges and universities, and discusses how to improve the effectiveness of online course resources for reference.
The Effects of Deleveraging Policy on Cash Dividends

This paper examines the effect of deleveraging policies on cash dividends by using the difference-in-difference (DID) model based on the panel data of Chinese A-share non-financial firms from the 2008-2020.The results indicates that the deleveraging policy can promote enterprise dividend, with a marginal utility of 0.06%.
On the Relationship between Vocational Education and the Development of Digital Economy

In the current context of China's economic development and transformation, the digital economy is experiencing rapid growth, with the proportion of GDP increasing year by year.Vocational education can provide human capital for economic development, and digital empowerment of vocational education requires that vocational education must keep pace with the times and technology.This paper analyzes the relationship between vocational education and the development of the digital economy, combines the shortcomings in the current teaching process in vocational colleges, and gives suggestions for the development of vocational education in the context of the digital economy, so that vocational education and the development of the digital economy can coordinate and promote the development of the entire social economy.
Application of ARCS Motivation Model in the English Writing Teaching in Senior High School

Writing is the main manifestation of students' language output ability and can also raise awareness of how language works.However, the current difficulties in English writing teaching, such as students' insufficient motivation, teachers' lack of attention to English writing teaching and imperfect English writing evaluation mechanism, have made the development of students' writing ability a major difficulty in English teaching.To address this situation, this paper attempts to integrate the four types of motivation strategies of ARCS model into senior high school English writing teaching in order to effectively motivate and maintain learners' enthusiasm for writing and improve the effectiveness of writing teaching.
The Literature Review of Unmanned Aerial Vehicle (UAV) Industry

The development of unmanned aerial vehicles (UAV) originated in the 1920 s of the 20th century and was used for air strikes on the battlefield.In the following decades, unmanned aerial vehicles were developed and used for military activities many times.At present, the energy supply types of mainstream unmanned aerial vehicles are divided into three types: battery power supply, liquid fuel power supply and hybrid oil and electricity power supply.However, the current mainstream unmanned aerial vehicle energy supply system still has disadvantages, so in the future, the development direction of unmanned aerial vehicles must be the lack of improving unmanned aerial vehicle Systems, and innovation is achieved in unmanned aerial vehicle technology with new technology.The commercial and military development of drones depends on new technology, this paper aims to review the current situation of unmanned aerial vehicle industry and predict the future development trend and direction of unmanned aerial vehicle industry in combination with the current situation.
Research on the Application of Mao Zedong's Poems and Artistic Songs

In the new era of multicultural integration, Mao Zedong's poems and artistic songs as an important part of vocal music teaching in colleges should be valued for their moral value.Because it is not only conducive to improving students' singing ability, but also conducive to the inheritance and development of national culture.This paper will study the characteristics of the music style of Mao Zedong's poems and artistic songs, and focus on its role and value in vocal music teaching in colleges and universities, and put forward some suggestions for improving the teaching strategies of Mao Zedong's poems and artistic songs in vocal music teaching in colleges.
Evaluation system and application of classroom teaching reform of Bridge Engineering based on classroom simulation subject competition learning

The simulated subject competition of college students plays an extremely important role in cultivating students' self-learning ability, innovative thinking, practical ability and innovative ability.The teaching of bridge engineering specialty is based on the discipline competition, aiming at the cultivation of application-oriented talents, and exploring the multi-angle and multi-level professional talent cultivation mode from theory to practice.This paper considers the existing problems and puts forward the training mode of "simulating subject competition learning in class" to train students' engineering practice ability and innovation and entrepreneurship ability.
Design and implementation of artificial intelligence fusion experimental platform based on machine learning algorithm

The platform is a platform for learning artificial intelligence algorithm services.It provides users with artificial intelligence algorithm learning tools, visual algorithm models, drawing functions, online running code and other functions.In addition, users can exchange their learning experience and experience with other users in the platform through the learning communication page.Through this platform, users can not only learn and apply artificial intelligence algorithms, but also have other functions, such as blog communication, function image drawing, neural network structure visualization, intelligent voice assistant and virtual interactive assistant, which are functionally perfect and easy to use.There is a tremendous talent gap in the field of artificial intelligence in China.The platform can effectively help learners learn artificial intelligence.Algorithm visualization makes learning more visualized.The interesting function of the platform increases entertainment, promotes users ' learning motivation, and adapts to the current development of our country.
From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm

Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a differentiable function involving the matrix exponential trace $\mathrm{tr}(\exp(\cdot))$, opens up a way to learning DAGs via continuous optimization, though with a $O(d^3)$ complexity in the number $d$ of nodes. This paper presents a low-complexity model, called LoRAM for Low-Rank Additive Model, which combines low-rank matrix factorization with a sparsification mechanism for the continuous optimization of DAGs. The main contribution of the approach lies in an efficient gradient approximation method leveraging the low-rank property of the model, and its straightforward application to the computation of projections from graph matrices onto the DAG matrix space. The proposed method achieves a reduction from a cubic complexity to quadratic complexity while handling the same DAG characteristic function as NoTears, and scales easily up to thousands of nodes for the projection problem. The experiments show that the LoRAM achieves efficiency gains of orders of magnitude compared to the state-of-the-art at the expense of a very moderate accuracy loss in the considered range of sparse matrices, and with a low sensitivity to the rank choice of the model's low-rank component.
An Efficient Steganographic Approach for Securing Text Transmission Using S-Box in Cloud

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack.
Conceptual Cognitive Model for Human-Machine Social Contact and Emotion based Generated Behaviors

The development of a machine’s motivating factor is the focus of current scientific study on human-machine social interaction. These motives provide the machine important reasons to accomplish its objectives. Intrinsic motives create feelings in a person during any human-to-human social encounter, allowing that person to control and re-generate their conduct in accordance with the ongoing social exchange. "Theory of Mind" is a psychology term that refers to a person’s capacity to comprehend an adversary’s perspective and alter his or her behaviour accordingly. Such motivating robots are needed in the current human-machine social interaction because they may adjust their behaviour to maximise the value of their responses by observing the ambient emotions of the other person. This impression is optimistic for the machine’s embodied learning for its "Theory of Mind"-based social interaction. For a computer to create contextually based learning, the suggested study defines a conceptual cognitive model for the machine. By taking into account the emotions of the interactive human and using a recurrent tapping delay line neural network, the machine is able to control its own internal mental states.