Reviewerly/exHarmony huggingface dataset:
A Method for Real-Time Planning of Hypersonic Cruise Trajectory

In order to reduce the time required of avoiding the no fly zone in the process of trajectory planning for hypersonic cruise vehicle, and realize the real-time for cruise trajectory planning, a real-time trajectory planning method of combined A* search algorithm and geometric curve planning method is proposed. The same structure of search unit in A* algorithm is changed to anomalous structure. By the advantage of search speed of A* algorithm, and combined with the geometric curve planning method, the rapid trajectory planning method based on geometric curve and A* algorithm of anomalous structure is formed. The results show that the trajectory, which can effectively avoid the no fly zone can be obtained by using this method. This method can also obtain flight commands, and can realize the real-time requirements of trajectory planning.
Teaching Case Study on Educational Reform Based on Hierarchical Domain Knowledge Reconstruction

As the half-life period of knowledge is getting shorter and shorter, the mutual transformation between scientific research results and teaching content is an effective means to keep students updated with knowledge. Based on the principle of knowledge classification, this study takes the law of students' acceptance of knowledge into account and resolves knowledge into four “domains” of different levels: the basic domain, simulation domain, design domain, and exploration domain, so as to reconstruct the teaching content. In the basic domain and simulation domain, knowledge is mainly the traditional basic theoretical knowledge and the most advanced scientific results. In the design domain and exploration domain, students transform from knowledge recipients to knowledge applicators and developers, thus realizing the mutual transformation between scientific research results and teaching contents. Years of teaching practice shows that the reconstruction of teaching content based on Hierarchical domain knowledge has a remarkable effect on improving students' independent innovation ability.
Understanding Agile Software Development Team Adaptation Processes

This study is about team adaptation from a continuous change perspective. The purpose was to investigate how agile software development teams in a Nordic financial institution adapt to task-based triggers. A single case study was conducted of three software development teams in a product unit reporting that they worked in agile ways. One of the main findings is that the agile software development teams sought to define tasks to be worked on individually. The adaptation triggers occurring during taskwork mainly prompt task-related interaction. In addition, the findings support that these adaptation triggers can either prompt small adjustments to taskwork or be more severe and move the focus to evaluation and planning activities before taskwork can be continued. We propose a model describing how agile software development teams adapt to task-based triggers based on the findings.
An image recognition method based on transfer learning

Traditional neural networks need a lot of data set training in feature extraction and image classification tasks, and classification tasks are often not completed in small sample tasks. This paper uses transfer learning methods to design the traditional network VGGNet-16 to avoid the need to train the network from scratch Complexity. According to the classification target, VGGNet-16 is fine-tuned to obtain the network used for this classification task. The effect of different Dropout values on the model is verified in the fully connected layer. The results show that migration learning has excellent training effects for small data samples and reduces training costs. After adding the Dropout layer, the performance of the network model is not only improved by 1.1 %, but also more stable than the network model without the Dropout layer. The data shows that the network model is more stable when the dropout is 0.5 and the batchsize is 32, and the accuracy rate reaches 97.10%.
Demystifying Augmented Reality (AR) in Marketing From the E-Commerce Perspective

Augmented reality (AR) technology has been widely used in various business applications in the past five years. Since the beginning of 2020, with the COVID-19 pandemic and its impact on various industries, AR has become one of the technologies that have significantly reduced physical interactions between buyers and sellers. This chapter reflects its finding in fours areas: 1) eCommerce mobile AR apps can allow customers to better interact with products virtually. 2) AR facilitates customer shopping journey in three stages of purchasing, namely before-purchase, purchase, and after-purchase stages. 3) Design and develop mobile AR apps with two features of virtuality and interactivity to the extent that enables customers to favor AR apps to the offline shopping experience in a sustainable trend. 4) Online retailers can utilize their AR apps to predict target customers' preferences, hence giving them effective promotions to motivate them to buy their preferred products online.
Research on Projection Interaction Based on Gesture Recognition

With the continuous emergence and vigorous development of various new technologies in the information age, human-computer interaction is also changing constantly. People are increasingly connected with the virtual world, so it is necessary to study more natural ways of human-computer interaction. Projection interaction is one of the most important interaction methods. The purpose of this paper is to study how to overcome the misoperation problem caused by foreign objects (interferers) in the environment of projection interaction (Fig. 1c). The method (named after FDGR) adopted is to use the fingertip detection algorithm based on user habits in image processing and the gesture recognition algorithm based on Leap Motion to complete the accurate and convenient natural human-computer interaction. Through user experiments, the experiences of completing the fixed assembly task in the mode of projection (P), gesture (G) and FDGR (PG) were compared, and the data was processed and analyzed. The experimental results showed that FDGR could effectively overcome the misoperation caused by foreign objects (interferers) and the non-intuitiveness of simple gestures, and effectively improved the user's operating experience.
Composition of Similarity Metrics for Correspondence Matching in Depth Estimation

Correspondence matching is a prerequisite step in dense depth estimation techniques. In this paper we consider various similarity metrics for correspondence matching and we present an approach which can be used to optimize it. Experimental results show that by careful selection of similarity metric can have positive impact on depth estimation quality and that the differences between various metrics range up to 60 percent points of bad-pixel depth map quality ratio. It has also been shown that usage of proposed composite similarity can lead to improved depth map quality, expressed as lower bad-pixel ratio.
Analysing the Impact of Climate Change on The Crop Yields of Irrigated Crops and their Water Requirements in India Using Neuro Evolutionary Algorithm

Global food security, facing the threat of climate change, is one of the major challenges of the 21st century for sustainability that is already under scrutiny with adequate food security for population growth. Current challenges include water shortages, unaffordable costs due to production and demand, and unpredictable weather conditions. They urged farmers to provide improved farming. In particular, uncertainty about climate change, lack of irrigation facilities, lack of land resources, and lack of crops from traditional farming techniques. In agriculture. Different machine learning techniques such as perception, division, regression, and aggregation are used to predict crops. Artificial networks, vector support systems, linear and linguistic lines, decision trees, and Bayesian intelligence are some of the mathematical methods used to implement the prediction. Obviously, choosing the right algorithm for the sample design algorithms presents a challenge for researchers about the selected plants. In this work, research has been conducted on how many algorithms of machine learning can help predict the dissolution of crops. Methods have been developed to generate predictions. Use machine learning technology in a large computer control system.
Bengali Words Classification by Its Prefix Using Machine Learning Classifiers

Classification techniques are used to classify the given data input to their desire class or target. By using classification techniques in machine learning the data can be given their classes. By keeping that in mind we focused to our Bengali word which have Bengali prefix. There are 21 prefix in Bengali grammar which have a great impact on Bengali grammar. All Bengali people are familiar with all the Bengali prefix. To classify those words by their prefix we used most common machine learning supervised algorithms. There are more than 500 data in the data set where all the data is arranged by the Bengali prefix. Classes are given by their desired prefix. After the preprocessing of data machine learning supervised algorisms are applied. Most common supervised algorithms like KNN, DT, NB, RF, LR, XGB classifier, and SVC classifier for getting more satisfying outcome are applied. There are a lot of detailed information which are described specific segment of this paper. Random forest and support vector classifier is outreach to the prominent accuracy among more than 90%.
Trusted Transaction and Sharing for Industrial Internet Data Security

Secured transaction and sharing for Industrial Internet data security fundamentally guarantees the free and orderly flow of data and the value of the productive factor. In this paper, developments and practices in trusted transaction and sharing in China and other countries are studied. Risks, status quo, and trends are summarized. Key technologies and product systems are analyzed. Models and technology architectures are designed. The mechanism and modes of promotion are presented. In addition, suggestions on how to improve trusted transaction and sharing are also proposed.
Improving Classification Performance of Deep Learning Models Using Bio-Inspired Computing

Deep learning models have paved the way towards generating high-efficiency classification systems for multiple applications. These applications include lung disease classification, electrocardiogram classification, electroencephalogram classification; forest cover classification, etc. All these applications rely on efficient feature selection capabilities of deep learning models. Models like convolutional neural network (CNN), recurrent neural networks (RNNs), long-short-term-memory (LSTM) etc. are used for this purpose. These models tend to evaluate all possible feature combinations via iterative window-based feature processing. Thereby trying to cover indefinite number of feature combinations in order to classify a definite number of features into a definite number of classes. All these models have a stopping-criteria, which depends upon the error rate difference of previous current iteration. If the error rate is less than a particular threshold, and number of iterations are above a certain predefined value, then training of these networks is stopped. This property of deep learning models limits their real-time performance, because training stops even if the accuracy is lower than expected. The reason for this low accuracy is high dimensionality of search space, due to which selection of the most optimum features is skipped. In order to reduce the probability of such conditions, this text proposes a bio-inspired Genetic Algorithm model for accuracy-based feature selection. The selected features are given to different deep learning models like LSTM RNN, and their internal performance is evaluated. Here, heart failure disease dataset from kaggle is used, and it is observed that due to pre-feature selection process, overall accuracy of these models is improved by 10, while precision, recall fMeasure scores are improved by 15 for heart disease data sets. The specificity and sensitivity performance is improved by 20 when compared with RNN and LSTM models individually.
Evaluation of Spare Parts Configuration and Maintenance Strategy of New Air Defense Anti-missile Radar based on Improved AHP and Cloud Gravity Center Theory

Based on the characteristics of spare parts configuration and maintenance strategies of the new type of anti- missile Radar system, the evaluation index system of the spare parts configuration and maintenance strategy of the new type anti -missile radar is constructed. The GRG method is integrated into the AHP method to calculate the weight of each index in the index system. Based on the theory of cloud gravity center, the performance evaluation cloud model of spare parts configuration and maintenance strategy for new anti-missile radar is constructed. The evaluation of spare parts configuration and maintenance strategy by the cloud model is finished, the feasibility of the method is verified by an example.
Comparison of Clustering Algorithms Using KNIME Tool

Development of present-day methods for logical information gathering has brought about substantial scale collection of information relating to different fields. Web mining is an exceptionally hot research area which joins two of the actuated research topics DM and WWW. The most perceived approach is to classify Web mining into Web content mining, Web structure mining, and Web usage mining. Clustering is appropriate part of Web mining. Clustering is a task to mine information, and a typical strategy to measure information which is utilized as a part of various fields, including ML, design acknowledgment, photograph investigation, data recovery, and bioinformatics. In this paper, the comparison of different clustering techniques using KNIME tool is implemented. The algorithms analyzed are: K-means and FCM on different types of dataset.
Semantic Based Recommendation System for Predicts the Continuous Next Page Link Using A Novel Pruned Techniques with Brilliant Markov Model (PTBMM)

Web Page Prediction is utilized to foresee the following arrangement of web pages that are needed by the client. Prediction of the client's conduct is based on the client's previous history and information. Web is excessively enormous and complex such that client gets confounded in the web and can't get the appropriate outcome. Notwithstanding, the prediction has simplified it for clients to get the necessary outcome all the more efficiently from the web. There are various techniques created for better prediction of clients' conduct and to give clients the following arrangement of web pages that the client can use for navigation. In this paper, we proposed a Semantic-Based Recommendation System (SBRS) for Predicts the Continuous Next Page Link Using a Novel Pruned Techniques with Brilliant Markov Model (PTBMM). To quantify the performance of the Artificial Neural Network (ANN) Trained SBRS various standard performance measures, for example, Precision, Coverage, F1-Measure, and R Measure have been utilized Parameters and shows that the proposed work is superior to different methodologies currently being used.
Design and Validation of Low-Power Secure and Dependable Elliptic Curve Cryptosystem

The elliptic curve cryptosystem (ECC) has been proven to be vulnerable to non-invasive side-channel analysis attacks, such as timing, power, visible light, electromagnetic emanation, and acoustic analysis attacks. In ECC, the scalar multiplication component is considered to be highly susceptible to side-channel attacks (SCAs) because it consumes the most power and leaks the most information. In this work, we design a robust asynchronous circuit for scalar multiplication that is resistant to state-of-the-art timing, power, and fault analysis attacks. We leverage the genetic algorithm with multi-objective fitness function to generate a standard Boolean logic-based combinational circuit for scalar multiplication. We transform this circuit into a multi-threshold dual-spacer dual-rail delay-insensitive logic (MTD3L) circuit. We then design point-addition and point-doubling circuits using the same procedure. Finally, we integrate these components together into a complete secure and dependable ECC processor. We design and validate the ECC processor using Xilinx ISE 14.7 and implement it in a Xilinx Kintex-7 field-programmable gate array (FPGA).
Pell hyperbolas in DLP–based cryptosystems

We present a study on the use of Pell hyperbolas in cryptosystems with security based on the discrete logarithm problem. Specifically, after introducing the group structure over generalized Pell hyperbolas (and also giving the explicit isomorphisms with the classical Pell hyperbolas), we provide a parameterization with both an algebraic and a geometrical approach. The particular parameterization that we propose appears to be useful from a cryptographic point of view because the product that arises over the set of parameters is connected to the Rédei rational functions, which can be evaluated in a fast way. Thus, we exploit these constructions for defining three different public key cryptosystems based on the ElGamal scheme. We show that the use of our parameterization allows to obtain schemes more efficient than the classical ones based on finite fields.
An Adversarial Network Embedded with Attention Mechanism for Pancreas Segmentation

Pancreas segmentation plays an important role in the diagnosis of pancreatic diseases and related complications. However, accurately segmenting pancreas from computed tomography (CT) images tends to be challenging due to the limited proportion and irregular shape of pancreas in the abdominal CT volume. To solve this issue, we propose an adversarial network embedded with attention mechanism for pancreas segmentation in this paper. The involvement of generative adversarial network contributes to retaining much spatial information for segmentation through capturing high dimensional data distributions due to its competing mechanism between the discriminator and the generator. Furthermore, the application of attention mechanism enhances the interdependency among pixels, and thus containing contextual information for segmentation. Experimental results show that our proposed model achieves competitive performance compared with most pancreas segmentation methods.
Groundwater Heavy Metal Contamination Mapping Using Geographic Information System (GIS): A Case of Nashik Thermal Power Station, Eklahare, Nashik (M.S.), India

Coal-based thermal power plants generate a significant amount of fly ash and are facing fly ash management problems, which is emerging as a major environmental concern. Commonly, fly ash is disposed of in an unlined earthen pond, which may lead to contamination of groundwater in surrounding areas due to the leaching of heavy metals through fly ash. In the present study, hydro-geochemical analysis is carried out on groundwater samples from the proximity of Nashik Thermal Power Station, Eklahare village, to assess the impact. The heavy metals, i.e. Cd, Hg, and Pb, are selected and analysed in groundwater samples during pre-monsoon and post-monsoon seasons due to its high concentration in coal and fly ash samples. The analysis results showed that the maximum concentration of Cd is 0.0068 mg/l and 0.017 mg/l; Hg is 0.36 mg/l and 0.032 mg/l; and Pb is 0.23 mg/l and 0.075 mg/l during the pre-monsoon and post-monsoon seasons, respectively. Around 33.33%, 58.33%, and 41.67% of samples in pre-monsoon have concentration beyond desirable limit of the Bureau of Indian Standard, 2012 for Cd, Hg, and Pb, respectively. While 58.66%, 66.67%, and 50% of samples in the post-monsoon season have exceeded desirable limits. Spatial distribution maps for selected heavy metals are prepared using the IDW data interpolation technique in GIS. The maps showed that the ash pond is adversely affecting the groundwater quality in the village, thereby creating a threat to the health of residents and indicate the need for corrective measures to check the infiltration of leachate from the ash pond. Based on contamination zones, the location of a new groundwater source can be planned.
Study of intelligent recommendation for online video courses

In order to improve the accuracy of online video course recommendation, this paper proposes a method combining Latent Dirichlet Allocation (LDA) model and artificial neural network for intelligent recommendation. Firstly, the online text comments of each course are extracted, and an LDA model containing topics and words is constructed. Then, according to learners' browsing history and online behavior, artificial neural network model is used to analyze learners' preferences and mine their learning preferences. Finally, the LDA model of learners' subject words is established and applied to intelligent recommendation courses. Experimental results show that our method can effectively recommend appropriate courses to learners.
Robustness Testing Framework For Neural Network Image Classifier

Abstract Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interference intensity, a variety of robustness test sets are constructed, and a robustness testing framework suitable for neural network classifiers is proposed. And the feasibility and effectiveness of the test framework and method are verified by testing LENET-5 and the model reinforced by the adversavial training.
Computer simulation of adapting the object image selection method to video surveillance conditions

Abstract The article deals with the issues of computer modeling of methods for selecting images of objects against a non-uniform background. A test video sequence with given background and object parameters is considered, which provides imitation of one of the special cases of video surveillance conditions, namely, the convergence of the video surveillance point and the object. The issues of adaptation of the compensation selection method to the specified conditions of video surveillance are discussed. Examples of test images and graphs of dependences of the probability of correct determination of coordinates depending on the value of the local contrast of the object in relation to the background, obtained by computer simulation are given.
An Fusion Algorithm of Improved A* and IDWA for Dynamic Path Planning

Aiming at the problems of path redundancy, unsmooth path and low real-time obstacle avoidance rate in the path planning , a dynamic path planning algorithm based on improved A* algorithm and improved dynamic window approach is proposed. First, the search method of the A* algorithm is extended to 7*7 neighborhood search, and then the path is secondarily planned by removing redundant nodes. Secondly, the weighting coefficient of adaptive heading angle is used in the evaluation function of dynamic window approach, so that the robot can automatically adjust the steering according to the change of environmental obstacle rate. Finally, a global optimal evaluation function is designed based on the dynamic window approach of adaptive heading angle weighting coefficient, the path node of the improved A* algorithm and final position are added to the evaluation function. Simulation results show that the algorithm optimizes the path length by 9.2% and reduces the planning time by 51.13%.