Reviewerly/exHarmony huggingface dataset:
An improved A* algorithm for UAV path planning

The main task of path planning is to provide a safe and feasible path for robot navigation. Although conventional A* algorithm can provide a distance-optimized path, there are some collision hazards when UAV cannot follow path perfectly. The proposed algorithm adds the obstacle information to the evaluation function of A* algorithm to avoid obstacle-dense area for planning. Besides, in order to compensate computing time increased by the improved evaluation function, an extended search step size is added to the A*, which performs better in the obstacle-sparse area. And simulation results also prove the above advantages.
On the Issue of Advanced Training of Primary School Teachers

The article presents the results of an empirical study, in the process of which, by means of questionnaires, it was found out how younger students perceive the image of a teacher. Based on the opinions of the respondents, a conclusion is made about what accents should be highlighted in the preparation of a future primary school teacher in the context of mastering teaching and educational activities. The main characteristics of the advanced training of a teacher are highlighted: practice-oriented, anthropological and motivational orientation.
AI Applied in Computer Network Technology Analysis and Research

Abstract In the big data era, the level of information technology in China has been greatly improved, and AI technology has emerged as the times require. It has now been widely used in computer networks, effectively improving the overall work efficiency and quality of staff. The AI technology application has greatly changed the working methods of the people. The production and application of AI has prompted a clear development direction for science and technology in the big data era. In the development of modern society, the application of AI in computer network technology it is of great significance. The thesis mainly discusses the analysis of AI in computer network technology.
Evolving Deep Neural Networks for Collaborative Filtering

Collaborative Filtering (CF) is widely used in recommender systems to model user-item interactions. With the great success of Deep Neural Networks (DNNs) in various fields, advanced works recently have proposed several DNN-based models for CF, which have been proven effective. However, the neural networks are all designed manually. As a consequence, it requires the designers to develop expertise in both CF and DNNs, which limits the application of deep learning methods in CF and the accuracy of recommended results. In this paper, we introduce the genetic algorithm into the process of designing DNNs. By means of genetic operations like crossover, mutation, and environmental selection strategy, the architectures and the connection weights initialization of the DNNs can be designed automatically. We conduct extensive experiments on two benchmark datasets. The results demonstrate the proposed algorithm outperforms several manually designed state-of-the-art neural networks.
Structuring Online Communities

Problem definition: Online Question and Answer communities were started to supplement customer support services. In contrast to conventional customer support, users in online communities can post questions, and other users with more experience or knowledge can answer these questions. Generally, questions answered get rewards and visibility in the community, while the askers gain knowledge if their questions get answered. We study how users decide to join, leave, and participate in these communities. We link the user participation decisions to the underlying network structure of the community. Finally, we explore the levers a community designer can use to balance user participation level and the community’s efficiency in providing answers to users’ questions. Methodology/Results: We model the community as a multistage stochastic game where all the users have different skill levels. We find the stationary equilibrium of this game and theoretically show that only a core-periphery network structure can emerge in such communities. This network structure has been empirically observed in most online communities. Furthermore, we find that increasing the cost of asking questions in the community improves the proportion of askers that get answers to their questions. This results in higher user satisfaction. However, a higher asking cost lowers the participation level in the community. This trade-off between participation and community efficiency results in non-monotonicity in the number of users in the community with the participation cost. Managerial implications: The paper explores the cost of asking a question as a lever that can be used by communities to control the number and knowledge type of users in the community. The communities typically operationalize higher asking costs by either directly penalizing question asking activity or setting up stricter guidelines for questions to be answered. We find that increasing the cost of asking is not always bad for the community. In fact, a higher asking cost improves user satisfaction which can lead to an increase in the number of users in the community despite higher asking cost. We also discuss how the existence of low knowledge users in the community (and not necessarily the high knowledge users) is essential to the survival of such communities.
Facial Expression Recognition Based on Hybrid Attention Mechanism

With the rapid development of science and technology, facial expression technology is gradually applied to many aspects of human life. Through the research of facial expression recognition technology, this paper discusses the application of facial expression recognition in intelligence education. This paper presents a facial expression recognition model combining Attention Mechanism and Convolutional Neural Networks. This model combines channel attention and spatial attention, and can quickly find the places that need attention in the image. It extracts important features in the image according to the weight of information, weakens the useless information, improves the efficiency of network operation, and improves the drawbacks of traditional convolutional neural networks. CK+ dataset is used to cross-validation the model. The data is divided into training set and validation set in a 4:1 ratio. The samples are normalized first, then the normalized data is input into the model, and the validity of the model is verified by a comparison test. The experimental results show that the model proposed in this paper has better efficiency and robustness than other models, and can achieve better results in the actual situation and meet the actual needs of human beings.
Generalized Proofs of Knowledge with Fully Dynamic Setup

Proofs of knowledge (PoK) are one of the most fundamental notions in cryptography. The appeal of this notion is that it provides a general template that an application can suitably instantiate by choosing a specific relation. Nonetheless, several important applications have been brought to light, including proofs-of-ownership of files or two-factor authentication, which do not fit the PoK template but naturally appear to be special cases of a more general notion of proofs of knowledge or possession. One would thus expect that their security properties, in particular privacy and soundness, are simply derived as concrete instantiation of a common generalized PoK concept with well understood security semantics. Unfortunately, such a notion does not exist, resulting in a variety of tailor-made security definitions whose plausibility must be checked on a case-by-case basis.
Heuristic Detection of Pharma Temperature Anomalies Using IOT and Machine Learning Techniques

In pharmaceutical companies, during the manufacturing of tablets, early detection of an error occurred on the temperature basis plays a crucial role in terms of public life as well as the investments done on its manufacturing. Anomaly detection during manufacturing is a complex task. It is very difficult and also not accurate to detect the damage occurred using traditional methodologies these days. Forecasting the anomaly before it becomes massive is important, for example, the Chernobyl nuclear disaster. Mechanistic models are known to be demanding in computational manner. Hence, a model that can detect an anomaly at the earliest possible stage and also affordable in all possible ways is of a very high demand. The field of machine learning and Internet of things has received much interest from the community of scientists. Due to the ease of implementation of machine learning in various fields, it is of sheer interest to study whether an artificial neural network or some machine learning algorithms can be a best model for the detection of anomalies regarding threshold frequencies of temperature in combination with Internet of things. Implementing the incentives lay down by government and any sorts of temperature limit (Threshold frequency) within the given span of time can be easily achieved by the model proposed, wherein it collects the temperature data for every particular elapse of time and analyzes it using machine learning algorithms. In case of any anomalies detected, it immediately alerts the people by any kind of general-purpose input and output like sound, light, or message alert.
The Tuition Payment Queuing System Uses Android-Based First in First Out (FIFO) Algorithm

Abstract Queuing is an important component of a process in service. Queues are formed in a company or institution due to customers. Disorderly queues are a big problem for all concerned. Where, disorderly queues occur at University Prima Indonesia at the time of tuition payment so that the campus has to carry out a crowded queue process and students have to stand waiting for the next call resulting in less effective and efficient even waste of energy for students and employees. This research designed a system of tuition payment queues using the First In First Out (FIFO) algorithm which aims to reduce the time students are queuing at the checkout at the time of tuition payment so as not to wait too long so that the time of students and campus employees is not wasted too much and reduce the crowded queue time. The result of this study is an android-based tuition payment system, which is useful for reducing student build up when paying tuition. Thus it can be said that the calculation result of P0 queue has a result of 0.25%, and the number of customers 3 people, the average customer in the queue has a value of 2.25, the total customer time is 20 minutes, and the average customer time in the row is 15 minutes, the server busy time is 0.75, and the server’s empty time is 0.25%.
An FPGA-Based Hardware Accelerator for 2D Labeling

The 2D Labeling algorithm is used in many applications due to its superior image processing quality. As the requirements of image processing continue to increase, generating huge computational workloads, the efficient real-time implementation of this algorithm is very challenging. In recent years, research on accelerating 2D Labeling algorithms on GPUs has made rapid progress. However, GPU devices usually bring a large amount of energy consumption and are therefore not suitable for a wide range of applications in embedded scenarios. In this paper, we propose a highly integrated general-purpose hardware accelerator for medical image processing to effectively improve the computational performance of 2D Labeling algorithms and reduce the power consumption of FPGA devices. The design integrates image denoising, edge detection, and image segmentation algorithms in a hardware IP core based on a deep pipelining framework, which can effectively improve the speed of 2D Labeling algorithm during intensive medical image processing through parallel computing and data reuse. The design is implemented on Xilinx ZYNQ XC7Z020, and we consume very less energy and improve the computational performance by 1.3 and 2.1 times, respectively, compared to the software design based on advanced NVIDIA GeForce GTX 1660 Super and Intel(R) Core (TM) i7-10700 CPUs.
Stylometric Analysis of Writing Patterns Using Artificial Neural Networks

Plagiarism checkers have been widely used to verify the authenticity of dissertation/project submissions. However, when non-verbatim plagiarism or online examinations are considered, this practice is not the best solution. In this work, we propose a better authentication system for online examinations that analyses the submitted text's stylometry for a match of writing pattern of the author by whom the text was submitted. The writing pattern is analyzed over many indicators (i.e., features of one's writing style). This model extracts 27 such features and stores them as the writing pattern of an individual. Stylometric Analysis is a better approach to verify a document's authorship as it doesn't check for plagiarism, but verifies if the document was written by a particular individual and hence completely shuts down the possibility of using text-convertors or translators. This paper also includes a brief comparative analysis of some simpler algorithms for the same problem statement. These algorithms yield results that vary in precision and accuracy and hence plotting a conclusion from the comparison shows that the best bet to tackle this problem is through Artificial Neural Networks.
A Tumor Image Feature Agumentation Algorithm based on Improved Convolutional Neural Network

Cell image-based disease prediction model is an important research in the field of artificial intelligence, which is based on the assumption of converting tumor images into matrices for classifying and learning matrix features in neural networks. However, the differences in the incidence of different diseases lead to insufficient data from medical samples, making it difficult train efficient and accurate prediction models. Therefore, the small data problem becomes an urgent problem for scientists to solve. In this paper, we propose a feature augmentation algorithm based on an improved visual geometry group (VGG19) neural network, which can augment the size and quality of the medical image dataset and avoid the overfitting problem of disease prediction models due to insufficient medical image dataset. We obtain the feature-augmented dataset for traditional machine learning model. The feature augmentation algorithm set out in the present paper improves in accuracy, f-score, and recall metrics.
Detection of feature-poor, small sized objects on noisy images

Abstract Visual identification of objects is an important challenge today. Main target of frequently applied methods is to identify or classify complex objects. These methods are far less effective when objects are small and less complex, and thus less descriptor features are on hand. The main reason for this is that these features can significantly change on object occlusion or appearance of noise. The presented solution performs identification of simple, small (size is 17 × 13 pixels) objects with elliptical shape. High pass filtered normalized cross correlation is used for region of interest detection and a simple deep neural network is used for classification of selected regions. The proposed method detected objects on a noisy image with accuracy of 96.2%.
The Effect of Using Kahoot Application To Teach Grammar Eighth Grade Students in Junior High School

Grammar is the most important aspect to be studied and explored, because using the correct grammar will form and build a correct sentence. In the Industrial Revolution 4.0 era, which affects the education system by using technology, one of which is by using the Kahoot application in the learning process. The research population includes eighth grade students, consisting of 18 students at SMP Muhammadiyah 9 Boarding School Tanggulangin in the academic year of 2020/2021. This study uses a pre-experiment that uses one class. the average value between the pre-test and post-test experienced a difference. the average value of the pre test is 46.66 and the average value of the post test is 76.94. The significant value of the research was 0.000 &lt; significant level 0.05, meaning that there is an effect of Kahoot application for teaching grammar of the eighth grade students at SMP Muhammadiyah 9 Boarding School Tanggulangin.
A Web Application for Prevention of Inference Attacks using Crowd Sourcing in Social Networks

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.
Research on GAN-based Text Effects Style Transfer

With the development of neural style transfer and generative adversarial network, the research of text effect style transfer has appeared. The text effect style transfer aims to render text images with style images to produce text effects images. However, for more complex text, the existing methods will generate unrecognizable font images. Therefore, we propose to add morphological methods to the glyph transformation to limit the degree of glyph transformation, and add distance transformation loss when training the texture network to limit the texture transfer, so as to improve the overall transformation effect. Experiments show that, compared with other existing technologies, our proposed method is more suitable for stylizing complex glyph images.
Research on Security Awareness to Protect Data Through Ontology and Cloud Computing

Nowadays, providing security for the data is the major concern especially in the cloud storage unit. Therefore, a security aware mechanism has to be developed to improve the security using ontology concepts in the cloud storage most of the existing servers used in the networks are used for storing and accessing the data, but these servers are built in a centralized manner, providing security for the data is a key requirement for the servers. The major threat in the existing servers is the data stored in the servers are subjected to hacking. This paper presents a technique to efficiently store the data in cloud by using concepts of ontology and Secret Sharing Scheme (SSS) has been introduced in the proposed framework to protect the data. This paper presents a model whereby the data can be securely stored for the longer duration in the distributed environments. Ultimately the proposed security scheme has achieved better performance and outperformed the conventional technique.
Educational and Ethical Aspects of the Technoscience Challenges in the University Landscape

Education and research are the main vectors that determine the development path of the university, while the transformations of the nature of science are so significant that in the field of education they find expression not only in the emergence of new disciplines and specialties, but also in the revision of the educational paradigm of a modern university. The paper analyzes the impact of transformations of modern science and methods of scientific knowledge production on the university and its research and educational strategies. In technoscience as a modern form of the post-non-classical paradigm of scientific rationality, science and technology form a hybrid of theoretical activity and social practice. Knowledge is produced not only in the context of discovery and underpinning, but also in the context of the estimated consequences of its application. It is shown that the challenges of technoscience, together with the dynamically developing global economy of knowledge, influence the development strategies of the university, which today performs not only research and educational functions, but also implements the mission of social responsibility, contributing to sustainable development, the introduction of responsible technologies and innovations, defending the status of knowledge as public good as opposed to the status of knowledge as social capital. The specificity of the technoscience ethos in the educational landscape of the university is updated, due to the transdisciplinary organization of scientific knowledge. Transdisciplinary research is characterized by a new vertical dimension that goes beyond the plane of object connections not only into the sphere of human as an agent, but also into the sphere of his life, practice, socio-cultural values, combining the complexity of the world with the complexity of human knowledge. The introduction of converging technologies is characterized by the unpredictability of undesirable side effects arising during their creation and operation. In a practical aspect, the ethics of technoscience is designed to contribute to the creation of mechanisms of self-restraint and self-control in conditions of uncertainty; it requires the researcher to be able to self-reflect on the methods, goals and consequences of the technologies usage. The task of a modern university in the context of the technoscience challenges is to cultivate a responsible professional, a person who is aware not only of the epistemic responsibility of a professional, but also of the internal, existential responsibility of a person as a global citizen.
An survey: Quantum Phase Estimation Algorithms

Quantum Phase Estimation is a basic algorithm for realizing the advantages of Quantum Computing. It is based on quantum Fourier Transform and is also a core part of quantum integer decomposition and quantum solving of linear equations. With the development of quantum cryptography and quantum machine learning, The content of this aspect is paid more and more attention and research. Therefore, this article summarizes and sorts out the relevant content of Quantum Phase Estimation algorithms in recent years. Firstly, the basic concept of Quantum Phase Estimation algorithm is introduced; secondly, the improvement, application and implementation progress of Quantum Phase Estimation algorithm in the past ten years are introduced; finally, the problems and challenges of Quantum Phase Estimation and prospects in various fields are summarized.
Assessment of Load Balancing Techniques in Cloud Computing

In today’s environment cloud computing is widely useful for end users to store and access large amount of data. CC has been seen as unrelated to technology that creates the next generation computer revolution and is rapidly becoming a hot topic in the IT industry. This fast-paced CC has fueled concerns about the key to success of information systems, communications, detection, social testing and integrity, data availability, the science system, and information security. Therefore, the study of CC has attracted much interest in recent years. In this paper, we aim to highlight current open problems and challenges in CC. We have discussed this paper three times: first we talk about CC and the many services it provides. Second we highlight a few security issues for CC based on its service level. We then point out a number of open challenges from the perspective of Cloud Computer Discovery and its future implications. This work includes the highlights of the platforms available in the present era of cloud development and research.