Reviewerly/exHarmony huggingface dataset:
Intelligent RRT Exploration Mapping Method Based on Evolutionary Cognition in Unknown Environment

How to make robots explore effectively in unknown environments is one of the key issues in robot navigation. Among them, the exploration of efficiency and traversal issues is the embodiment of effectiveness. To solve this problem, an Intelligent Rapidly-exploration Random Tree (IRRT) algorithm based on evolutionary cognition is proposed. This algorithm can intelligently adjust the growth rate of the global tree according to the complete situation of the exploration map. Using the method of simultaneous generation of global tree and local tree to explore boundary points, the detected boundary points are filtered by the DBSCAN clustering method to obtain the target point set, which is used as the current target point of the robot movement. Simulation verification in the Robot Operating System (ROS) shows that the algorithm can complete the traversal of the unknown environment, and effectively reduce the time cost.
Profit-Driven Service-Chain Deployment For EDA Requests On Private Cloud

Cloud computing is becoming a pervasive technology in many fields. However, the EDA field has not yet developed a working prototype that employs the commercial tools as services and deploys them on the cloud. Therefore, in this paper, we aim at three core problems: request acceptance, service schedule, and resource deployment on private EDA clouds, and give an integrated solution for processing requests. Compared to Conventional Inclusion (C.I.), total profit can increase under a high arrival rate by 29% after applying Progressive Inclusion (P.I.). Meanwhile, the non-preemptive, penalty-considered, Least Slack First Scheduling deals with the order of services to be scheduled. As to resource deployment, parcel-fit (PF) outperforms best-fit (BF) and first-fit (FF) by 5% and 18% on total profits under a high arrival rate. To sum up, the proposed solution provides an effective way to build a private EDA cloud with more profits than the conventional solution.
Power Equipment Identification Based on Single Shot Detector

In recent years, more and more deep learning technologies have been extensively used in various fields, such as intelligent medical, intelligent manufacturing, to realize the intelligence of various systems. To solve this traditional manual inspection of power equipment, we propose a method of intelligent identification of electrical equipment using a Single Shot Detector based on the light network—MobileNet V2. Through data enhancement and other technologies to make up for the lack of training data often encountered in deep learning. The average precision is 96.3%, and the average recall rate is 85.9% in this paper. It also has a good recognition effect for complex scenes. We provide a solution to equip unmanned aerial vehicles, remote cameras, inspection robots, and other resource-constrained devices with the power equipment identification system based on deep convolution network.
An Ensemble Feature Ranking Technique for the Analysis of the Craniofacial Growth

Several models are proposed for the predication of the human age and for the modeling of facial changes during formative years. Different ranking techniques are used to measure the importance of the proportion index features in the age detection problem, and each technique is resulting a different order for feature ranks. This work proposes a set of features for age detection and proposed an ensemble method to extract the most important features more accurately. This method detects a new ranking of the features based on the ranks resulted from three classical ranking techniques. The new list of highly ranked features extracted by the proposed ensemble method shows better results than the separate ranking techniques. Furthermore, the analysis of these features shows important conclusion about the changes during the craniofacial growth. Three benchmark datasets are tested in this work, and the prediction accuracy is presented and compared againest the previous work.
REAKTIVASI DAN PEREMAJAAN PERPUSTAKAAN SDN 07 BEKASI JAYA

Library is a means in which reading activities occur. The library includes a room, part of a building or a separate building that contains collection of books, which are arranged and arranged in such a way, so that it is easy to find and use when needed by the reader. With the school environment library, students can add insight and knowledge outside class hours. The implementation of this activity is filing books, labeling books, tidying books on the shelves according to the collection, repainting the library room and giving a touch of wall stickers on the side of the room. After carrying out the “Library Rejuvenation” activities for SDN 07 Bekasi Jaya students there was an increase in the number of free visits to the library from before the library rejuvenation was carried out. SDN 07 Bekasi Jaya students are happy with the library rejuvenation.
A Shilling Attack Model Based on TextCNN

With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 – value as the evaluation index.
Improving Deep Neural Network Robustness with Siamese Empowered Adversarial Training

Deep neural networks (DNNs) have been widely used in many critical application domains. However, the security of DNNs are threatened by adversarial examples, and the problem has not been fully solved. In this work, observing the deficiency of existing adversarial training methods, we propose a new adversarial training method with a specially-crafted contrastive loss and the siamese training architecture. Our method fully leverages the distance relationships between the benign examples and adversarial examples. We conduct extensive experiments to evaluate our proposed design. The results show that the DNN models trained by our methods are more robust to adversarial examples compared to those been trained with other SOTA methods.
Functionally Enhancement of IPNP(Image Processing Network Programming) for Modifying Appearance Inspection Program

In this paper, we discuss a method for automatic programming of inspection image processing. Generally, an automatic appearance inspection in a manufacturing process has been expected from a viewpoint of reliability and efficiency of the inspection. However, the automation of the appearance inspection using image processing technology is not always easy. This is because building the system requires proficiency in selecting the image processing logic and tuning the parameters. Therefore, we had proposed a method for automatic programming of inspection image processing system called "Image Processing Network Programming (IPNP)". In this study, we extend the functionality of IPNP. Concretely, that function is modifying an appearance inspection program automatically when it fails to detect a defect region. It is verified from experiments that the proposed method can modify inspection image processing programs.
Research of Intelligent Temperature Measuring Robot System

Abstract With the outbreak of novel coronavirus pneumonia worldwide, a kind of epidemic prevention robot is urgently needed in society. The intelligent temperature measurement robot system mainly includes the robot’s high stability multi-attitude omnidirectional movement module, robot path planning and autonomous navigation module, robot environment information acquisition and recognition module, robot remote control module, and robot system integration. According to the design principle of the wheeled robot, high stability and multi attitude omni-directional mobile platform of temperature measuring robot are constructed; path planning and autonomous navigation of robot are realized based on sensor data fusion; temperature detection and remote care function are realized by thermal imaging sensor system by capturing the thermal radiation emitted by the human body, and based on open modular control system architecture. Based on the existing research foundation, the intelligent temperature measurement robot system is established by integrating the robot multi attitude omni-directional mobile system, robot path planning, and autonomous navigation system, robot environment information acquisition and recognition system, and robot remote control system.
Implementation of Gradient Descent Method by Optical Treatment

A method for optical processing to estimate gradients is proposed. This method is based on phase modulation of optical signals and optical Fourier transform. The procedure of the method is quite similar to that with a quantum algorithm. One of the advantageous features of the proposed method is suitability for simple implementation. Also, this method can be extended to gradient descent. The extended method is explained and verified by numerical analysis.
An Opinion and Context-Aware Inverted Index Graph Model for Sentiment Search and Analysis

Two major functions required in textual data modelling and knowledge discovery especially in modern customer interaction and research are search and sentiment analysis. Recent research has however not been able to combine these two in a way that instils flexibility into the iterative process of textual analysis for decision making. In this work, the Opinion and Context-Aware Inverted Index graph model is proposed for performing sentiment search and analysis on textual data. Unlike previous works, the use of the proposed model retrieves needed information without requiring re-processing of sentiment information. The proposed approach clarifies the difference between sentiment search and sentiment categorization. Furthermore, it shows how to perform opinion aware search based on graph traversals, pattern matching and aspect opinion filtering. To evaluate the flexibility and capabilities of the proposed scheme, the feature interest range, customer feature satisfaction/dissatisfaction intensity, feature co-mentions, and sentiment based ranking metrics of various bipartite projections for feature co-mention sub graphs using PageRank are extracted from a well-studied dataset on online product reviews with an extension of the proposed model. The chosen design and methodology is shown to be suitable for big data scale implementations.
A Novel Approach of Text Encryption Using Random Numbers and Hash Table

In modern days, word files are used to carry out information storage and other multiple operations. The word file allows the users to write valuable text and information in plain text format. When such documents unknowingly falls in the wrong hands. Stealing of that valuable data is obvious. To prevent such situation, this paper proposes a cryptographic technique which uses random key values to generate ciphertext in the docx file itself. Then it uses inverted hash table to translate this ciphertext numbers into hash values. A hash table is re-created at the receiver side. In the whole process a key = (a, b) along with the hash key = (h) is transmitted to the receiver for decryption. The key values are generated by Random Key Generator (RKG) which uses a table to generate random key values based on the initial seed values. During decryption same sequence of key values are generated. The hash table translates the hash values into ciphertext numbers and then returns back the original plain text. The strength of the cryptosystem has been analyzed on various criteria and parameters which gave very promising results.
Running to Get Recognised

This research investigates the use of Convolutional Neural Networks (CNN) and specifically, You Only Look Once ver. 4 (YOLOv4) to detect Racing Bib Numbers (RBNs) in images from running races and then to recognise the actual numbers using Optical Character Recognition (OCR) techniques. Pre-processing and Tesseract OCR were employed to achieve this. Using a self-acquired private dataset we achieve a recall of 0.91, precision of 0.88, an F1-measure of 0.89, and mean average precision (mAP) of 0.935 for detection. Full number recognition of 71% is then achieved on the successfully detected RBNs. Additionally, the proposed approach attains a very low average inference time of 23.5 ms compared to a previous best recorded time of 750 ms. This is achieved this with a relatively small training set of 1374 images, where previous research used 498,385 labelled images.
An Improved DWKNN-Based Rock Fall Distance Prediction Model

In this study, we present a rock fall distance prediction model by improving the DWKNN algorithm. In this model, a method to measure the distance of falling rocks by slope height is proposed, which is a relatively simple and practical approach; the rock fall weight, slope height, slope angle, equivalent friction coefficient, horizontal bounce height and vertical bounce height are chosen as the main influencing predictors of the slope rock fall distance. Then, we apply the prediction model to the prediction of rock fall distance. The training samples are collected from 50 typical cases of rock fall while the predicting samples are 10, and the test results are determined by cross-training for 5 times. By comparison with other three popular methods, our proposed prediction model is more accurate. At last, the accuracy of our proposed model is verified by a series of laboratory tests, and the experimental results show that our proposed model is reliable.Keywords- DWKNN algorithm, rock fall distance, prediction model, laboratory test
ORGANIZATIONAL-PEDAGOGICAL ACTIVITY OF I. P. LVOV

Аrticle described the activities of I. P. Lvov as the head of the department of pedagogy of the Chernigov Pedagogical Institute. It were studied the handwritten labor of a teacher in conducting state examinations, analyzed the annual reports of the Department of Pedagogy, compiled by I. P. Lvov during the 1950-1951,1951-1952,1952-1953 school year, the plans of the department and the lesson plan with students of the 2nd year, Ukrainian and Russian departments of the Faculty of Humanities. A personal qualities of the teacher were analyzed in the several archival documents. It showed that I. P. Lvov was a wise teacher and morally honest to students and colleagues, intellectually creative to support the department of pedagogy, as well as the laboratory of psychology with it with all the necessary equipment for students' independent education. He was quick-witted, presented innovative pedagogical ideas and introduced them into the pedagogical process of the department of pedagogy. I. P. Lvov took the initiative to realization educational activities among students such as reports, lectures on the plans of public education authorities, school education authorities, school administration, parental committees and leadership of pedagogical circles. I. P. Lvov considered it necessary that teachers of the institute read popular lectures to parents of students and thereby pedagogue them. Students of the institute were supposed to get involved in the work of class teachers, to carry out various types of classroom and extracurricular activities with pupils. Report on the educational and methodical work of the Department of Pedagogy was analyzed At the Department of Pedagogy under the leadership of I. P. Lvov, working curricula for all disciplines of the department were heard in due time at regular meetings. I. P. Lvov in the report indicated positive moments about the educational activities carried out during the school year by teachers of the department of pedagogy to change the working plans of the department.
Research on Node Sorting Method for Aviation Network Based on Kernel Extreme Learning Machine

Correctly sorting the nodes of aviation network by technical method has important practical significance and application value for normal network operation. A node sorting method based on Kernel Extreme Learning Machine (KELM) is proposed for aviation network. Firstly, it evaluates the comprehensive importance of nodes through complex indices, and then the simple indices such as node degree value, point strength, and K-shell value are selected, finally KELM is introduced to establish the mapping relationship between simple indices and comprehensive importance. The simulation shows that it is effective and feasible to apply the method to sort the nodes of aviation network.
Low-Complexity DNN-Based End-to-End Automatic Speech Recognition using Low-Rank Approximation

Targeting the on-device speech-to-text application for streaming inputs, this paper presents an efficient way to reduce the computational complexity of deep neural networks (DNNs) for attention-based speech processing. The proposed technique applies the singular value decomposition (SVD) to the large-sized matrix multiplications, removing less important computations by utilizing the low-rank approximation. The clipping thresholds are carefully adjusted to relax the computing costs as well as the memory overheads while maintaining the recognition accuracy.
Evaluating image features in real-world scenarios

The rapid growth in the autonomous vehicle technology field has triggered multiple advances in feature-based computer vision applications. However, most applications are based on using optimal data, for example traffic data under sunny conditions and regularly do not include adverse weather or low-light conditions. This paper presents a survey describing the effect of real-world perturbations on the performance of 6 conventional feature-based methods and 2 convolutional neural network (CNN) methods in 3 scenarios: during one year, during one month and all-year weather and illumination changes. An evaluation is made on the suitability of the selected methods, using the amount of extracted inliers and processing times as metrics for a low-, medium and high-complexity case. It is shown that perturbations real-world conditions exceed the performance differences between conventional methods and that a performance increase is achieved with a well-tuned CNN at the expense of one order of magnitude of processing time.
Tiled-pyramidal whole slide image creation with constant memory complexity

Whole slide images are a important class of images with usage in medicine, biology etc. Pyramidal image storage formats enable access to these images over internet with minimal data transfer and latency. Existing techniques to create these pyramidal formats require large amount of physical memory and storage. They are also often limited by processing bottlenecks. In this work we propose a new algorithm for creating pyramidal images that ameliorates these drawbacks. The proposed method enables creation of arbitrary large pyramidal images on low end systems for the first time.
Multi-Feature Extraction and Fusion Method for Pedestrian Re-identification

Pedestrian re-identification is to identify the target interested from pedestrian images taken by multiple cameras. Recently, the ReID (Person re-identification) algorithm has shown that the local features of pedestrians are used to describe various parts of the body, the global features of pedestrians are used to represent the overall information, and the local features of relationships are used to make certain connections between local features to form more discriminative features. Although these algorithms have a certain effect on pedestrian re-identification, their recognition accuracy is still not satisfactory. To solve these problems, we propose a novel multi-feature extraction fusion model (MFEFM). It can extract three different features of pedestrian images at the same time and merge them together to form a more discriminative feature. First, use ResNet-50 as the infrastructure to extract basic features. Then, global maximum pooling (GMP) is used to extract local features of pedestrian images, global average pooling (GAP) is used to extract global features, and pose-estimator is used to extract key point features in parallel. Finally, we use the relationship network to form connected local features and key point features, and then connect these three features together.
Effect of Hyperparameter Optimization technique to Extract the Nuclei from the Cell

In this modern era of computer vision technology, image processing plays a vital role in the medical field, and the analysis of medical and biological images through the implementation of image processing is a challenging task along with it also helps the doctors to diagnose the disease as well as start treatment against the disease. In computer vision, especially image processing applications, image segmentation is considered an essential part of this method in which partitioned the digital image into various small segments and from these segments extract the specific object for further investigation with the help of deep learning. The extraction of nuclei from the cell using these modern technologies make a faster method that enables a more rapid cure finding process and reduces the time-to-market of the new drug. At the same time, the accuracy is considered the critical element because reliability and acceptability are based on it. This paper helps in extracting the nucleus from the cell by using image processing techniques and the power of deep learning architectures while it is based on the U-Net nuclei segmentation framework, but the standard model achieved 84.8% testing accuracy, achieved 83.5% training accuracy and also little bit variation in predictive resultant image as compared to binary image but with a hyperparameter optimization technique, the model achieved 97.6 % testing accuracy and 97.5 % training accuracy and the predictive resultant images of the hyperparameter optimization model are more accurate as compared to without a standard model and help researchers community of medical imagery analysis.