Reviewerly/exHarmony huggingface dataset:
Intelligent Driving System for Hot In-place Recycling Heating Vehicle

In order to improve the harsh working environment such as high temperature and soot faced by the operators of hot in-place recycling heating vehicle, and to increase the intelligent level of heater control, an intelligent driving system composed of a differential GPS positioning system, a path planning system, a vehicle control system and a management center was designed It uses coordinate transformation and the least square algorithm to achieve high-precision path fitting. The Newton method is used to calculate the deviation from the point to the fitting curve, and a double closed-loop PID controller is used to achieve accurate path tracking. The field test results show that the vehicle can be controlled by intelligent driving system to walk along the pre-construction route, and the lateral control accuracy is within ±30mm, which meets the requirements of the hot in-place recycling construction process.
A Temporal Action Detection Model With Feature Pyramid Network

To find out all actions included in an untrimmed video, temporal action detection localizes the starting and ending of each action, and identify their categories, simultaneously. Different with trimmed video which always involves a single action instance, the untrimmed video is much more complicated. That is, there are not only multiple action instances, but also multiple background clips among action instances. This complexity presents a great challenge to temporal action detection. Structured Segment Networks, SSN, a recently presented temporal action detection method, constructs a two-stage pyramid structure to obtain temporal features of an action instance to complete its classification and location. SSN works well except that there are multiple action instances varying greatly in amplitude and duration. This paper introduces a feature pyramid network in the feature extraction phrase of SSN to expand the receptive field of the network to obtain features with different scales to predict action completeness, category, and boundary, respectively. Compared with the original SSN and other existing models, experiment results on dataset THUMOS14 shows the effectiveness of our method
Design of High Capacity 8 colors Code based on Screen-Camera Communication

Screen-Camera communication is a visible light communication mode based on camera-screen link, which needs a coding scheme with high capacity. Recently, numerous researches have been done on how to improve the capacity of barcode. In this paper we propose a new code with high capacity, which is generated with 8 channel colors to expand the carrying capacity. The capacity of traditional barcodes is limited by functional graphics, so the high capacity 8 colors code (HC8C code) utilized a new functional graphics to expand the capacity. This new functional graphics is comprised of four-color circles, which can locate and adjust the code quickly and occupies smaller area. Results show that at the size of 21 * 21 pixels, the capacity of the HC8C Code is 1200 bits. At the same size, the capacity of HC8C Code is 5.55 times larger than the QR Code and 1.63 times larger than the eight colors HD2DC Code.
Actual Measurement and Analysis of Power Transformer Dynamic Stability Diagnosis by Low Voltage Reactance Method Based on Short Circuit Test Check

With the continuous development of power system, transformer, as the key equipment in power system, plays an increasingly important role, and its safe operation is directly related to the reliability of the whole power system. In this paper, the relationship between transformer winding deformation degree and short-circuit reactance variation is discussed by electromagnetic calculation method. Through the electromagnetic calculation of transformer leakage reactance, the specific expression of winding reactance is given, which shows that transformer winding deformation will inevitably lead to the change of short-circuit reactance. The theoretical basis of measuring reactance and no-load current and other related parameters by low-voltage method before and after transformer short-circuit current impact, in order to diagnose whether the winding and core have abnormal changes, and the practical significance of carrying out the above detection are expounded.
High-Capacity Reversible Data Hiding in Encrypted Images by Second MSB Replacement

The technique of data hiding reversibly in encrypted images may be considered as an efficient method that can be used to hide data within an image that is being encrypted. In prevailing methods of data hiding, as in any other encryption standards, an image that has to be transmitted is encrypted first. This encryption is done by the sender by using any encryption key that is unknown to anyone accessing the image. It can be possible to introduce or cover information within the image that has been encrypted. This can be done without knowing any information about the image or about its encryption. On the receiver side, a recipient can decode the image if he has the encryption key or can retrieve the hidden message if he has the data hiding key. The methods must be able to perform a high payload compensating a high quality of image that has been reconstructed. Here various techniques that use reversible data hiding are reviewed in this paper and this leads to bitwise prediction data hiding.
A Systematic Mapping Study of Cloud Business and Legal Implications

Cloud computing can be considered a disruptive innovation or technology that is making life easier for Cloud users. Figuring out a focal point of research in a specific area of study particularly on cloud business and legal implications could sometimes be challenging. This paper conducted a systematic mapping study of Cloud business and legal implications. The method utilized three classes which are topics, research and contribution facets. The topics were extricated from the primary or essential studies, while research type like evaluation and contribution type namely tool was used in the analysis. The result indicated that there were more publications in relation to legal framework and tool with 3.2%, more papers in terms of legal framework and process with 4.0%, more work was done with respect to data protection and process with 5.6%, more papers on security and model with 14.4%, more publications on privacy and model with 8%, and more papers on confidentiality and method with 6.4%. The findings of this work ascertained multiple gaps that will be beneficial to providers, practitioners and researchers.
Cluster Analysis of Student Scores Based on Global K-Means Algorithm

Aiming at the problem that the initial clustering center in the K-means algorithm is easily affected by outliers, it is proposed to analyze the data based on the global k-means algorithm. The global k-means algorithm is used to improve the determination process of the initial clustering center and reduce the influence of the random initial clustering center on the clustering result. First, preprocess the data of the four courses of the first semester of the 2018–2019 academic year for undergraduates, and save them in csv format and then, through the experimental comparison of the real traditional k-means algorithm and the global k-means algorithm, we get the clustering indicators such as Jaccard coefficient, accuracy, F value, etc. The experimental results show that the global k-means algorithm is 7.9% more accurate than the original k-means clustering algorithm, and it is verified that the global k-means clustering algorithm is better than the traditional k-means algorithm.
Statistical Data Analysis

In the chapter, we will review concepts and techniques to carry out an analysis with the Wolfram Language, as well as perform a linear adjustment through equations and implement specialized functions of the Wolfram Language for the same purpose. With the use of statistical functions. The Wolfram Language is a useful tool for statistics and probability. Mathematica has the functions to perform numerical and approximate calculations for descriptive statistics and random distributions, random numbers and random sampling methods, as we will see on this sectoin.
Slant Split Criterion Random Forests Classification Algorithm based on Soft Margin Hyperplane

In order to further to improve the classification accuracy of the random forest algorithm, an Slant Split Criterion Random Forests based on Soft Margin Hyperplane (SSCRF) algorithm is proposed. In the decision tree node splitting process, consider using multiple attribute soft margin hyperplane of the decision tree to replace a single attribute splitting criterion, so that the splitting criterion becomes an inclined hyperplane in the data space, which effectively improves the accuracy of decision tree classification; In order to make the algorithm suitable for multi-classification problems, the "one-to-many" strategy is adopted in the process of node splitting; in the process of transforming into leaf nodes, various adaptive weights are assigned to improve the algorithm's classification ability. The experimental results show that the SSCRF algorithm has higher classification accuracy than the random forest algorithm.
Artificial Intelligence-Based Scribe

Scribe is used for helping physically challenged and short-term disabled students for instance, fracture. And also, the scribe is less efficient as they have major problems like lousy handwriting, and the student may have an introverted feeling due to their presence. These drawbacks of the manual scribe can be avoided with the help of an automated AI scribe. To build this AI scribe, this paper makes use of machine learning (To convert the text to their equivalent handwriting), Google speech to text API (Convert student speech to text), and Tobii Eye-tracking technology (To enable students to draw diagrams).
Interpretable Saliency Map for Deep Reinforcement Learning

Abstract Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.
An Overview of Fake Face Detection Approaches

Human face has been one of the most popular objects in computer vision. With the advancements in computing power over the past decades, deep face understanding systems have reached (and surpassed at some cases) human performance and hence, the problem of facial analysis has changed its route from analyzing to generating. These adversarial generation techniques have been used in generating fake news, fake political and financial statements, and even fake porn videos, leveraging the wide availability of large-scale public databases and machine learning techniques. However, there are only a few studies to detect the generated face from real. This study investigates the abundantly used fake face generation models and examines the techniques for detecting facial manipulation.
Segmentation and classification of hand symbol images using classifiers

The first form of human communication was reliant on signs, which is predominant in mute people; however, numerous variations of gesture-based communication are accessible. This chapter proposes a method for the classification of hand symbols. First, the quality of an acquired image is improved through preprocessing. Then, a segmentation technique is proposed for extraction of the region of interest and a feature extraction algorithm is applied to generate a feature vector. Finally, classifiers are trained with appropriate feature vectors and tested in terms of accuracy. The results of five classifiers are compared on the basis of image features.
Image Segmentation Based on Interval Type 2 Fuzzy Clustering and Differential Immune Clone Algorithm

The segmentation of brain magnetic resonance (MR) images plays an important role in the computer aided diagnosis and clinical research. However, due to presence of noise and uncertainty on the boundary between different tissues in the brain image, the segmentation of brain image is a challenging task. Many variants of standard fuzzy c-means (FCM) algorithm have been proposed to handle the noise. In this paper, a new method based on interval type 2 fuzzy clustering and differential immune clone algorithm for image segmentation is proposed. By replacing hard clustering with fuzzy clustering through incorporating interval type 2 fuzzy clustering into differential immune clone algorithm, this algorithm can obtain more abundant clustering information. Specially, as the advantage of interval type 2 fuzzy set is processing uncertain data, the proposed algorithm is more conducive to solve the uncertainty problem. In experiments, the obtained segmentation results on MR brain image demonstrate the efficacy of the proposed algorithm and superior performance in comparison to existing segmentation methods.
WITHDRAWN: Analysis of bosom malignancy using supervised machine learning classifier

Chest peril tends to one of the deceases that makes a huge amount of passing’s dependable. It's a remarkable kind taking all into account, and basic part of ladies’ ruins world-wide. Plan and data attraction approaches are a convincing method to portray data. Target in medic field, where assumptions are ordinarily considering and evaluation to pick. In this research, appraisal preposition various AI figuring: Random Tres, Deep Neural Net (DNN), Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Bayes Net, C5.0. The standard goal is to survey the models in get-together all information concerning productivity and reasonableness of each figuring as for accuracy, precision, affectability and unequivocally. Our research shows that KNN gives the basic exactness (97.13%) with low slip up rate. All outputs are runs inside an entertainment air and drove in SPSS information mining contraption.
Manipulation of multipartite entanglement in an array of quantum dots

Multipartite entanglement is indispensable in the implementation of quantum technologies and the fundamental test of quantum mechanics. Here we study how the W state and W-like state may be generated in a quantum-dot array by controlling the coupling between an incident photon and the quantum dots on a waveguide. We also discuss how the coupling may be controlled to observe the sudden death of entanglement. Our work can find potential applications in quantum information processing.
Statistical and spatial information association for clusters number and mean values estimation in image segmentation

A straightforward way to assess the number of clusters as well as their mean values for clustering-based gray scale image segmentation initialization purpose, is presented in this paper. It is based on an association of statistical and spatial information of the image so that, the final classes in the segmented image correspond to “meaningful” modes in an histogram built from the image characteristics. The experiments testify the efficiency of this method in case of synthetic and real images.
Identification of Mongolian and Chinese Species in Natural Scenes Based on Convolutional Neural Network

Multilingual texts are very common in natural scenes. Dilterent languages have different shapes and structure. According to the language characteristics to choose the appropriate method can reduce the recognition error rate during text detection and recognition. This paper mainly proposes a lightweight convolutional neural network LTCNN for natural scene images mixed with Chinese characters and Mongolian. Firstly, collecting pictures contained both Chinese characters and Mongolian; then using perspective transformation to process the slanted text image, and using gamma transformation to enhance the image of underexposed or overexposed pictures;nest using the improved EAST model for the preprocessed pictures to extract text makes the data set needed for training the network; finally, the pictures in the data set are fed into the LTCNN network for training and identification of Mongolian and Chinese species. Experimental results show that the accuracy of Chinese and Mongolian recognition using this method can be close to 90% accuracy. Index Terms-language recognition,LTCNN,text correction.
Convolution Neural Network based Rain Noise Removal for Real Time Application

Abstract Image quality is affected by rain streaks that have an adverse effect on the applications which involves the ideas of surveillance, automatic path navigation. Hence it is important to process the information present in the images affected by water droplets caused by the rain or the mist. So this forms the basic need to identify the problem which does not have any mathematical correlation about the same. This research tries to work on the methodology which will denoise the criteria based on Convolutional neural network (CNN). CNN modelling helps in finding out the connections between the noise input image and rain streak. The proposed method uses synthetic database available publicly.
An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm.
An Effective Algorithm for Mining Indirect Association Rules

Abstract In order to solve the problem that it is necessary to scan the database many times and produce unnecessary frequent itemsets in the process of mining indirect association rules, a new algorithm FPI-mine based on FP-Tree is designed to mine the indirect association rules in transaction database. Firstly, FP-Tree is constructed, and then the indirect item pairs and intermediate support sets of all frequent items are found. Finally, all the indirect association rules are obtained by mining algorithm. It can directly mine indirect association rules without generating all frequent itemsets. Finally, the effectiveness of the algorithm is verified by experiments.
Convolutional neural network on embedded system

Context: The popularization of the use of Convolutional Neural Networks has created new challenges such as the use in embedded systems applications. Scientists, developers and organizations need to analyze the evolution of the field in industry and academia. Objective: To analyze the evolution of the area of Convolutional Neural Networks in industry and academia comparing the development between them. Methodology: A scientific mapping was conducted to analyze the scientific research in the field and a technological mapping was conducted to analyze the technological development in the field. Results: We identified 545 scientific papers and 33 patent applications. Conclusion: There is still a big difference between scientific development and technological development of the area, but the evolution of both is increasing.
Variational value learning in advantage actor-critic reinforcement learning

The performance and applications of reinforcement learning based on artificial neural networks (ANNs) have been limited by the overfitting problems of ANNs. With probability distributional weights and the variational inference technique, Bayesian neural networks (BNNs) can reduce the overfitting of data and thus improve the generalization ability of models. This paper proposes an advantage actor-variational-critic reinforcement learning algorithm (called A2VC) based on advantage actor-critic reinforcement learning (A2C). We model the value functions as a probability distribution and implement the distribution by the critic BNN. Based on the variational inference technique and the reparameterization trick, the weights of the critic BNN are well optimized. On the other hand, weights of the actor ANN are optimized with the stochastic policy gradient. Simulations in the lunar lander and cart-pole environments show the effectiveness and advantages of the proposed scheme over conventional A2C algorithm on the learning and decision-making capacity of agents.
The possibilities of criterion-oriented testing in verifying the achievements of teachers

The article presents the results of empirical research on perception of teachers from military engineering university about the features of didactic testing and on methodologies of diagnostics used by experienced and young teachers. It also presents the results of the survey on students’ learning motivation according to the achievement goal theory. Students have been identified as being oriented towards mastery goal, and concentrate on getting new knowledge and skills that lead to professional and personal growth. It has been proved possible to develop methods for determining the effectiveness of pedagogical action and communicative interaction between the teacher and the student using didactical criterion- oriented testing. One of the goals is to assess teacher’s performance as a factor influencing students’ cognitive activity, their results and motivation. The method of formalization by mathematical means of the modeling process and representation of criterion-oriented testing results as a communicative task of interaction between the teacher and the student is described. Prospects for use of theoretical bases, ideas and methods of educational evaluation in criterion-oriented testing and in verifying teachers’ achievements are discussed.