Reviewerly/exHarmony huggingface dataset:
Implementation of Mathematical Modeling Teaching Based on Intelligent Algorithm

The optimization of mathematical model plays an important role in the process of mathematical modeling. The optimization of intelligent algorithm is a common method to solve the optimization model. Its advantages are strong computing power, simple algorithm design and easy implementation. Because the objective function has no special requirements and is easy to program, it can solve the problem in an acceptable time range. Therefore, the intelligent optimization algorithm can well solve the optimization problem in mathematical modeling. Intelligent algorithms are well used in evaluation and determination, prediction and prediction, optimization and control models, especially through analysis and basic statistics. After rough statistics, in the college students' mathematical modeling contest every year, there are 49 problems including optimization problems, and the proportion is about 61.25%. At least one problem needs to be solved every year. In order to solve the optimization mode, students often face many difficulties in using intelligent optimization algorithm. Mathematical modeling course embodies the case education based on classification education, that is, knowledge classification. In education, more attention should be paid to the understanding and application of knowledge, the transmission and integration of knowledge. In education, we should choose the optimal algorithm together. The teaching methods should be varied. It is helpful to improve the teaching effect to explore the teaching method of intelligent optimization algorithm in mathematical modeling. This paper analyzes the problems in intelligent optimization algorithm education, and discusses the education methods of these problems.
USE OF GEOMETRIC TRANSFORMATIONS FOR THE DEVELOPMENT OF SPATIAL PERCEPTIONS IN KINDERGARTEN THROUGH THE IMPLEMENTATION OF COMPETENCY-BASED LEARNING APPROACH

Information about the paper titled "USE OF GEOMETRIC TRANSFORMATIONS FOR THE DEVELOPMENT OF SPATIAL PERCEPTIONS IN KINDERGARTEN THROUGH THE IMPLEMENTATION OF COMPETENCY-BASED LEARNING APPROACH" at IATED Digital Library
OBE-CDIO-Based Digital Media Technology Professional Talent Skill Training System

In order to improve the skill training effect of digital media technology professionals, a digital media technology talent skill training system based on OBE-CDIO is designed. The system hardware is designed, which mainly includes power supply circuit, CPU minimum system, asynchronous serial port interface circuit, ST7290 LCD drive circuit, keyboard scanning circuit, single-chip microcomputer and Wi-Fi module. On this basis, the system software is designed, mainly for the upper computer, lower computer and database of the system software. The experimental results show that the talent skill training system of this study effectively improves the theoretical and practical achievements of professionals, indicating that the system of this study can improve the skills of digital media technology professionals.
Detection and defense of cyberattacks on the machine learning control of robotic systems

Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.
Underwater active polarization imaging algorithm based on low-rank sparse decomposition

Underwater optical imaging has important application value, but it is also challenging. In traditional underwater imaging, the problems of uneven illumination, blurred texture details and low contrast often exist, in this paper we propose an underwater active polarization imaging algorithm based on low-rank sparse decomposition aiming to solve the problems above. According to the principle of underwater polarization imaging, the algorithm first performs target information enhancement on the acquired polarization images. Then combining with the low-rank characteristics of backscatter images in the scattered light field, the background information and target information could be separated from the captured images by the low-rank sparse decomposition principle, the high-quality image could be recovered from turbid water as a result. The results of experimental treatments with different turbidity levels demonstrate that the underwater polarization imaging algorithm based on low-rank sparse decomposition can improve the contrast of images, maintain the details of images and remove the background scattering at the same time. Moreover, the proposed method can effectively recover multiple targets and significantly improve the imaging quality which provides a new idea for underwater polarization clear imaging detection.
Predicting and Monitoring Bug-Proneness at the Feature Level

Enabling quick feature modification and delivery is important for a project’s success. Obtaining early estimates of software features’ bug-proneness is helpful for effectively allocating resources to the bug-prone features requiring further fixes. Researchers have proposed various studies on bug prediction at different granularity levels, such as class level, package level, method level, etc. However, there exists little work building predictive models at the feature level. In this paper, we investigated how to predict bug-prone features and monitor their evolution. More specifically, we first identified a project’s features and their involved files. Next, we collected a suite of code metrics and selected a relevant set of metrics as attributes to be used for six machine learning algorithms to predict bug-prone features. Through our evaluation, we have presented that using the machine learning algorithms with an appropriate set of code metrics, we can build effective models of bug prediction at the feature level. Furthermore, we build regression models to monitor growth trends of bug-prone features, which shows how these features accumulate bug-proneness over time.
Time-Optimal Paths for Simple Cars with Moving Obstacles in the Hamilton-Jacobi Formulation

We consider the problem of time-optimal path planning for simple nonholonomic vehicles. In previous similar work, the vehicle has been simplified to a point mass and the obstacles have been stationary. Our formulation accounts for a rectangular vehicle, and involves the dynamic programming principle and a time-dependent Hamilton-Jacobi-Bellman (HJB) formulation which allows for moving obstacles. To our knowledge, this is the first HJB formulation of the problem which allows for moving obstacles. We design an upwind finite difference scheme to approximate the equation and demonstrate the efficacy of our model with a few synthetic examples.
New improvement of "dynamic repulsive potential factor" on artificial potential field path planning algorithm

Path-planning as one of the most fundamental tasks of robots should cause less burden on calculation capacity and be efficient when the robots’ major tasks are not only about motion. Artificial Potential Field (APF) algorithm as an effective and simple path-planning method could meet such a requirement. However, the local minimum problem limits its application on robot’s path-planning tasks. To deal with this problem, this paper proposed a method named “Dynamic Repulsive Potential Factor” (DRPF). Compared to other existed improvements such as the sub-target strategy and combination algorithms with Rapidly exploring Random Tree (RRT), DRPF has the advantages of simple and effective. And the produced path of DRPF is shorter than the one produced by APF combing RRT, which is demonstrated in the experiment result. Meanwhile, the robot is continuing to move in the DRPF method while it is stopped when searching path in RRT, demonstrating the effectiveness on time aspect. The application of DRPF could meet the requirements of efficient and less calculation burden for path-planning function of robots whose major tasks are not only about navigation.
Real-Time Implementation and Analysis of Different Adaptive Enhancement Algorithms Using Embedded Hardware Boards

Contrast enhancement technology is used to improve perception and interpretability of the information from an image/video. Adaptive enhancement algorithms are used when a uniform approach of enhancement does not give the better results, and here, the method selectively chooses the optimum parameter to be updated and updates it simultaneously, thereby giving the best fit to the problems of real-time analysis of tracking, surveillance, etc. This paper compares the performance of six different adaptive enhancement algorithms three for gray-scale input and three for color input with real-time analysis by implementing these algorithms on embedded hardware boards, namely UDOO Quad and Nvidia Jetson TK1. In most of the existing papers, the analysis has been done on images, our approach is to implement these algorithms for real-time video and compare these algorithms, and we have used software and hardware optimization techniques for better real-time application to achieve better frame rates for these algorithms. In order to compare the performance of these algorithms, different quality metric parameters have been used. Many observations of different Quality Metric parameters have proved that Multi-Peak Histogram Equalization (MHE) (Antony, Mariena & Sathiaseelan, J.G.R.. (2018). Contrast Enhancement of Grayscale and Color images using Adaptive Techniques). For Gray Scale Video and Quadrant Dynamic with Automatic Plateau Histogram Equalization (QDAPHE) (Jagatheeswari P, Suresh Kumar S, Mary Linda M (2014) Quadrant dynamic with automatic plateau limit histogram equalization for image enhancement) for color video gives the optimum results for the analysis done.
Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach

One important task in the COVID-19 clinical protocol involves the constant monitoring of patients to detect possible signs of insufficiency, which may eventually rapidly progress to hepatic, renal or respiratory failures. Hence, a prompt and correct clinical decision not only is critical for patients prognosis, but also can help when making collective decisions regarding hospital resource management. In this work, we present a network-based high-level classification technique to help healthcare professionals on this activity, by detecting early signs of insufficiency based on Complete Blood Count (CBC) test results. We start by building a training dataset, comprising both CBC and specific tests from a total of 2,982 COVID-19 patients, provided by a Brazilian hospital, to identify which CBC results are more effective to be used as biomarkers for detecting early signs of insufficiency. Basically, the trained classifier measures the compliance of the test instance to the pattern formation of the network constructed from the training data. To facilitate the application of the technique on larger datasets, a network reduction option is also introduced and tested. Numerical results show encouraging performance of our approach when compared to traditional techniques, both on benchmark datasets and on the built COVID-19 dataset, thus indicating that the proposed technique has potential to help medical workers in the severity assessment of patients. Especially those who work in regions with scarce material resources.
Presenting Sense of Loud Vocalization Using Vibratory Stimuli to the Larynx and Auditory Stimuli

In recent years, technologies related to virtual reality (VR) have continued to advance. As a method to enhance the VR experience, we focused on loud vocalization. This is because we believe that loud vocalization can enable us to engage with the VR environment in a more interactive way. Also, as loud vocalization is an action that is thought to be closely related to stress reduction and a sense of exhilaration, the stress reduction through VR with loud vocalization is also expected. But loud vocalization itself has disadvantages for physical, mental, and social reasons. Then, we hypothesized that loud vocalization itself is not necessary for such benefits; but the sense of loud vocalization plays an important role. Therefore, we focused on a method of substituting experience by presenting sensory stimuli. In this paper, we proposed a way to present the sense of loud vocalization through vibratory stimuli to the larynx and auditory stimuli to users who are actually vocalizing quietly with the expectation for the sense of loud vocalization. Our user study showed that the proposed method can extend the sense of vocalization and realize pseudo-loud vocalization. In addition, it was also shown that the proposed method can cause a sense of exhilaration. By contrast, excessively strong vibratory stimuli spoil the sense of loud vocalization, and thus the intensity of the vibration should be appropriately determined.
Pengklasteran Area Pembangunan Bak Sampah Daun Pada Universitas Riau Menggunakan Metode K-Means

The area around Riau University has a lot of leaf litter due to its very large area,but currently the leaf waste scattered is only done burning. This is because there isno leaf garbage tub available in riau university environment and also there has notbeen an analysis of the grouping of areas that have the potential for the constructionof the leaf garbage tub. From the existing problems, it can be seen that RiauUniversity needs analysis of the grouping of areas to determine the grouping ofareas in building garbage cans. Therefore, this research aims to implement the kmeansclustering algorithm in conducting cluster analysis to determine the groupof areas in building garbage bins that exist in the area of Riau University. Thisstudy reviewed data in the form of 71 data point location of leaf garbage stackswith data attributes clustered are latitude points and longitude of data objects. Theresearch phase that emphasizes the analysis of k-means clustering goes through 2stages, namely the process of clustering with k-means, and the testing of clusteringresults with silhouette coeffisien. The clustering process is carried out byexperimenting the number of different clusters, namely k=3, k=4, k=5, k=6, andk=7. The best cluster results testing is done using the silhouette coeffisien method.Based on the results of the experiment, the number of k=3 is the best cluster withtest results of 0.5906023241609282. This means that as many as 3 garbage can bebuilt in the area around the centroid point of each cluster.Keywords : Clustering, Data Mining, K-Means, Python, Leaf Litter.
A distributed algorithm for state estimation of dynamic coupled and networked systems with cross‐correlated noises

Abstract We study the distributed problem for state estimation of dynamic systems, in which the system and measurement noises of each subsystem are cross‐correlated, and the local state of each subsystem is also coupled with that of the neighboring subsystems. By newly constructing measurements, the noises are uncorrelated, and we derive an algorithm only using the measurements and low‐dimensional information from neighboring subsystems. Furthermore, the obtained error covariance matrices have lower and upper bounds and the proposed algorithm enjoys local stability properties. In addition, two examples are shown the effectiveness of our algorithm when compared to the centralized Kalman filter, which is the optimal algorithm for entire information.
An Efficient Data Hiding Method for AMBTC Compressed Images

Data hiding techniques aim to embed the hidden data into digitalized carriers. In this paper, we present an efficient data hiding method for the Absolute Moment Block Truncation Coding (AMBTC) compressed images. The first step of the proposed method is performing the traditional AMBTC method, which is followed by an inner-block switching strategy and a quality improvement method. From the experiments, we demonstrate that the data capacity of this work is large, and meanwhile, the visual quality of the output stego image is satisfactory.
Application of Blockchain in Life Cycle Cost Management of Weapon Equipment

As the mainstay of a country's national defense force, weaponry plays an important role in the victory or defeat of the whole battlefield. This paper discusses the application of blockchain technology in the life cycle cost management of weapon equipment, focusing on the application of distributed account book, consensus mechanism and smart contract supported by blockchain technology in the life cycle cost management of weapon equipment.
The Establishment of Machine Translation Bilingual Corpus Based on Artificial Intelligence and Big Data Technology

With the rapid development of artificial intelligence and big data technology, all areas of society have gradually begun to use big data technology. Machine translation is a very popular and challenging research content in the field of natural language processing. From the idea of machine translation to the integration of syntactic information into machine translation, scholars at home and abroad have designed many formal models and algorithms for machine translation. They have made positive contributions to the research of machine translation in natural language processing and have made them increasingly mature. This article uses artificial intelligence big data technology to establish a machine translation bilingual prediction database. The content of the experiment is to conduct translation research on Chinese and English bilinguals through the machine translation bilingual prediction database. The main test is to test the accuracy of the number of sentences under different retrieval structures. Perform experimental tests on the recall rate, and analyze the data based on the test, and draw relevant conclusions.
Research on Power Line Communication of Energy Internet Based on Blockchain Technology

short-paper Research on Power Line Communication of Energy Internet Based on Blockchain Technology Share on Authors: Yixuan Guo Shanghai Lixin University of Accounting and Finance, China Shanghai Lixin University of Accounting and Finance, ChinaView Profile , Hongyan Ge Shanghai Lixin University of Accounting and Finance, China Shanghai Lixin University of Accounting and Finance, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1587–1590https://doi.org/10.1145/3482632.3483997Online:22 November 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
User Experience Design in Software and Hardware Components Studied in Human-Computer Interaction

User Experience (UX) is a concept based on the human-product interaction. An increase of UX studies in the Human-Computer interaction (HCI) field was observed in the last decade. Empirical studies based their experimental activity on HCI products, which are characterized by two components: Software and intangible (digital interfaces and web apps) and Hardware and physical (devices). Trough an explorative study, the authors propose a research direction to compares UX studies targeting software and hardware components of HCI products. A preliminary sample of papers was considered. The authors collected contributions where UX in HCI design is investigated through case studies involving devices with software and hardware components. Objectives, methods, and tools of each case study were compared. It emerged that complex systems require both quantitative and qualitative analysis approaches, as the wide variety of tools for data acquisition and processing show. Since Hardware components are more closely related to products such as consumer goods and engineering products, it is possible that methods and tools used to study hardware components could also be applicable to other physical and tangible products, i.e., the main reference for product, engineering, and mechanical design.
Application of Data Mining in College English Discourse Teaching

Classroom discourse teaching must conform to the principles of wholeness, psychology, schema theory and cognitive theory. Many students are not proficient in reading. Most of the time, they just stay at the superficial level of understanding sentences and do not have a comprehensive and profound understanding of the content through discourse analysis from a deep perspective. This paper constructs a classroom teaching mode guided by schema theory and cognitive theory, with discourse teaching method as the means and developing English ability as the index. This paper also analyzes the characteristics of mind mapping and discourse teaching, as well as the relationship between them. This paper discusses how to apply mind map in discourse teaching. The experimental results show that the teaching method proposed in this paper can be used for reference and inspiration to English teaching.
Trajectory Tracking Control for Intelligent Vehicles Based on Cut-In Behavior Prediction

For intelligent vehicles, trajectory tracking control is of vital importance. However, due to the cut-in possibility of adjacent vehicles, trajectory planning of intelligent vehicles is challenging. Therefore, this paper proposes a trajectory tracking control method based on cut-in behavior prediction. A method of cut-in intention recognition is adopted to judge the possibility of adjacent vehicle and the driver preview model is used to predict the trajectory of the cut-in vehicle. The three driving scenarios are divided to manage trajectory planning under different cut-in behaviors. At the same time, the safety distance model is established as the basis for scene conversion. Taking the predicted trajectory of the cut-in vehicle as a reference, the model predictive control (MPC) method is used to plan and control the driving trajectory of the subject vehicle, so as to realize the coordinated control of the subject vehicle and the cut-in vehicle. Finally, the simulation shows that the subject vehicle can effectively recognize the cut-in intention of the adjacent vehicle and predict its trajectory. Facing with the cut-in vehicle, the subject vehicle can take appropriate control actions in advance to ensure the safety. Finally, a smoother coordinate control process is obtained between the subject vehicle and the cut-in vehicle.