Reviewerly/exHarmony huggingface dataset:
Handwritten digit recognition based on multiple basic algorithms

To handle problems of data information recognition in various industries, the issue of handwritten digit recognition is solved in this paper by implementing four machine learning methods for the task of classification. First of all, four different classifiers have been implemented, trained and then the models would be tested. After that, the result is combined to make a final decision. The training data are giving as the MNIST training dataset, and the testing data are using MNIST testing dataset and USPS dataset, the four classifiers which are going to be implemented are Logistic regression, multilayer perceptron neural network, Random Forest model and Support Vector Machine model. Then the program of each algorithm generates the accuracy chart and the normalized confusion matrix to compare their performance in terms of different datasets.
Quadrotor trajectory planning for visibility-aware target following

In this work, we consider the challenges in the context of the quadrotor trajectory generation for the target-following tasks in cluttered environments where the task may be failed due to the occlusion of the mobile target by structures, i.e. pillars or wall corners. To address this problem, a model predictive control (MPC) based trajectory generation methodology of the quadrotor is proposed to autonomously follow a mobile target with considering flight safety, smoothness, and visibility in cluttered environments. The motion of the quadrotor is formulated as the boundary state constrained primitives (BSCPs), which are constructed offline with the dynamic programming method, and approximated by a pre-trained neural network (NN). Combining with the NN, the proposed method can efficiently generate the following trajectory that explicitly guarantees smoothness and kinodynamic feasibility. The numerical simulation and actual experimental results show that the proposed technique is highly effective. The demonstration video is available at: https://www.bilibili.com/video/BV1iq4y1S7he/.
An Approach Towards Human Centric Automatic Ontology Design

Given the magnanimous amount of data that the web stores and with the evolution of semantic web, appropriate techniques for management of semantic information become vital. The time of accessing the required piece of information defines the efficiency of a system. Ontologies play a very important role in defining and organizing the information segments of a domain, which in turn help improve the efficiency of retrieval of required information. In this paper, the focus is to densify and generate improved ontologies from adopted seed-domain ontologies by incorporating a framework that utilizes a multisegmented methodology involving the LSTM model for classification followed by the cuckoo search metaheuristic optimization algorithm and semantic similarity computation approaches such as Kullback–Leibler divergence and SemantoSim measure—a child approach of the commonly used WebPMI, to hold context and enrich the relatedness in the improvised ontologies. This human centric approach also implements various logic rules and agents to delicately handle the semantic data at the same time preserve its integrity. Domains adopted for the purpose of experimentation are ensured to be from diverse real-world topics. The efficiency of the proposed model is seen to be higher than the adopted baseline, and the former supported with an accuracy of 96.12% and a false discovery rate of 0.043, therefore, exhibiting clean success of experimentation.
English Translation Teaching System Based on Computer Multimedia Technology Information

Information education is the requirement of the times, and the integration of information technology and industry is inevitable. As an important part of the integration of information technology and classroom education, the subject computer-assisted teaching system has changed the traditional teaching methods and is in line with the current evolution of computer education. This article aims to study the English translation teaching system based on multimedia computer information technology. It expounds the research importance of English translation teaching assistant system, introduces the current popular intelligent assistant system, and analyzes the main research directions at home and abroad. Introduction to software platform and development tools. The development technology used in the design and implementation of the English translation auxiliary teaching system of the School of Foreign Languagesis explained in detail. Requirements analysis is the first step in software development. The development of each system must be analyzed in conjunction with the needs of the market and users before the development of the system can be carried out. The necessity of the last part is the detailed implementation of the English translation teaching system. The experimental results show that there are 21 exercises in the written translation information data that are greater than the threshold 0.3, which meets the exercise recommendation requirements.
Research on Personnel Location Method in Harsh Smoke Environment

In order to improve the efficiency of searching and rescuing on the spot in the harsh smoke environment, to speed up the evacuation and rescue and reduce property losses, we process the blurred image disturbed by the flue gas and describe the image which cannot be seen by the interference of the flue gas. Based on the Retinex theory and the MATLAB software, this paper studies and analyzes the video surveillance system's scene images in the harsh smoke environment. For example, when a large amount of smoke occurs in the scene during a fire, the scene surveillance image is blurred, and the exact location of the personnel cannot be identified to understand better. The fire scene‘s actual situation provides necessary technical support for guiding evacuation and rescue, allowing commanders to use a video surveillance system to observe the situation of the fire. We process the scene surveillance images and direct rescue personnel to carry out more effective and rapid rescue operations.
Research on Lossless Compression of Hyperspectral Images Based on Improved Deep Learning Algorithm

In order to improve the detection and recognition ability of multiscale hyperspectral images with weak edges, a lossless compression algorithm of multiscale hyperspectral images with weak edges based on improved deep learning is proposed. Using deep-level feature information extraction method to extract fine-grained features of cancer cells from multiscale hyperspectral images with weak edges, constructing 3D surface structure reconstruction model of multiscale hyperspectral images with weak edges, combining with variational Bayesian filter detection method to decompose multiscale color structure features of multiscale hyperspectral images with weak edges, and constructing gradually refined output structure model of multiscale hyperspectral images with weak edges, Fuzzy image coarse-scale neural network learning method is used to realize block matching and edge detection of multi-scale hyperspectral images with weak edges. Ultra-fine-grained features of multi-scale hyperspectral images with weak edges are extracted. Deep learning algorithm is used to adaptively optimize the lossless compression process of multi-scale hyperspectral images with weak edges, and the optimal design of lossless compression algorithm for multi-scale hyperspectral images with weak edges is realized. The simulation results show that the adaptive convergence of lossless compression of multi-scale weak edge hyperspectral images with this method is good, the feature resolution is strong, and the hyperspectral feature detection and recognition ability is good.
Analysis of Software Accounting Effectiveness on Data Sales in Bliss Kitchen

The development of the increasingly advanced business world will certainly always be related to technological developments, especially in the current 4.0 era. In Indonesia, there are various types of companies such as services, manufacturing, food, and others. The development of the company will cause more and more complex transactions. It is very unlikely that the company has to record all forms of operations with a manual system, especially for large companies with so much data that they need a system to help process data quickly so that all transactions can be processed into reliable information. complete and accurate so that the company's goals can be realized. The purpose of this study was to determine the effectiveness of accurate accounting software for recording sales reports in one of the companies that engaged in food & beverage, namely Bliss Kitchen. The research that the writer did used is a quantitative approach with a survey method. This study finds that Accurate software that applied in Bliss kitchen is effective. Evidence shows the results of the questionnaire from Bliss Kitchen employees is positive about Accurate.
Evolution of Automated Deduction and Dynamic Constructions in Geometry

Given its formal, logical and spatial properties, geometry allows an integrated framework where model theory and proof theory approaches can be explored. The development of geometry automatic theorem provingProving systems and dynamic geometryDynamic geometry, dynamic geometry , geometry software systems began as separated enterprises but its merging in integrated systems is currently doing its way. In this text, the history of automatedAutomated deductionDeduction in geometry is traced, from the early development of automatedAutomated theorem proversAutomated theorem provers, proving for geometry and from the emergence of the dynamic geometryDynamic geometry, dynamic geometry , geometry software systems, to the current status where different application systems combine dynamic geometryDynamic geometry, dynamic geometry systems, geometry software and automatedAutomated deductionDeduction. These tools enable their users to explore existing geometry knowledgeKnowledge in addition to creating new constructions and testing new conjectures.
1+1&amp;gt;2? Information, Humans, and Machines

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A Synthetic Data Generation Approach Using Correlation Coefficient and Neural Networks

Modern day machine learning models relies on huge amount of sample data to feed them, in order to give robust performance. Many studies have been conducted to show that if we have sufficient high amount of data, even simple models perform with great accuracy when used at very complex tasks. Having more data, both in terms of examples or more features, is fortunate. The availability of data enables more better insights and applications. More data indeed enables better approaches. Researchers in field of medical sciences require abundance of data to make intelligent models for disease classification. Generative Adversarial Networks (GAN) provides a way to generate data in order to tackle the above problems. In this paper, we explain an approach to generate synthetic data using machine learning techniques and correlation coefficients. In this study, we used Pearson correlation coefficient to determine the predictive relationship among the independent variables. An artificial neural network (ANN) model was created and deployed to generate data based on the relationship coming out from the correlation coefficients among the independent variables and also with dependent variables. The approach was tested on Boston House Price Dataset to generate synthetic data for different attributes. Also, to study the appropriateness and cogency of the generated data, we conducted comparison of two models in which one was fed with the real data, and the other model was trained on real and synthetic data. The study showed an improvement in accuracy and generalization on new training instances in the model trained with additional synthetic data. The results conclude that the generated data from the neural networks has sufficient efficacy which can be used for various purposes including data analysis and statistics.
Object Recognition and Multiple Image Stitching

Images are used in diverse engineering works and scientific research. But, every image has a particular resolution and size. Image stitching is one such possible way which can increase the resolution and size of image. This is a technique through which we can join multiple images which has some common key point and can make the image a wide-angle image. It could be one of the inexpensive ways to determine the wide-angle view. Cases like landslides, earthquakes, or any natural disaster could affect a wide area, but when we go for survey or estimation of damage, it can be an expensive method to arrange helicopters and gather the view of the place. Their image stitching ought to play an inexpensive and essential role. This report also focuses on the improved and efficient methods of traditional image stitching and reducing the errors. The old-style image stitching results are based on methods like feature point extraction and it results to some extent of misrepresentation errors, which is frequently occurred due to extended image arrangements. To provide high-quality image, an improved algorithm is proposed. The test aftereffects of improved algorithm have demonstrated that we can quicken the productivity of picture merging process, and furthermore lessen the all-encompassing distortion blunders. Lastly, object recognition can make the stitched information more informative by labeling. Using image stitching and object recognition, we can get multiple achievements helping us to get high-resolution panoramic view, increasing our possibility to get a wider view of any. These UAVs will help in getting the view from top in cases like earthquakes and landslides loss estimation. Using Internet of Things and machine learning, it can be achieved very efficiently.
Design of Multimedia Teaching System for Animation Production Based on Virtual Simulation Experiment Platform

Because of the incompatibility of animation data format, it is difficult to meet the needs of students in the effect display of animation production. By using the virtual simulation experiment platform to convert the format of animation data, coordinate system and size, to ensure that the system is suitable for different types of animation files and improve the display effect. The test results show that students are satisfied with the display effect and application value of the design system, and the system has been recognized by students.
Robust Local PMU Measurement Recovery Based on Singular Spectrum Analysis of Hankel Structures

The development of cyber-physical power systems gives rise to the concerns on the data quality issue of phasor measurement units (PMUs). Low signal-to-noise ratios (SNRs) and data missing caused by malicious electromagnetic interference, false data injections, and equipment malfunctioning may jeopardize the integrity and availability of power system monitoring, protection, and control applications. To ensure grid resiliency, this paper proposes a PMU measurement recovery method based on the singular spectrum analysis of Hankel structures. It utilizes single or multiple channels of PMU time-series to restore the problematic phasor measurements with low SNRs and data losses. Numerical case studies demonstrate that the proposed method can recover the noise-contaminated measurements with higher accuracy than traditional methods such as filtering and wavelet analysis, and can effectively complete the missed data. The latency margins of power system applications can be satisfied in the mean time.
Research on Acousto-optic Joint Detection and Visual Diagnosis Method of Power Equipment

Inclusions, pores, and cracks in the insulation materials of electrical equipment is increasing, leading to the continuous occurrence of insulation cracking and burning, which seriously threatens the safe and stable operation of the power grid. In the cause of above problems, this paper proposes a joint detection and diagnosis method for the outdoor insulation status, using image matching and image fusion technology to fuse the ultrasound and ultraviolet results, and thus quickly locate the corona discharge of high-voltage devices. First, based on the optimized multiple signal classification (MUSIC) algorithm and the ultrasonic single helix sensor array, an ultrasonic positioning visualization method of corona discharge is proposed. Subsequently, based on the refracted light path realizing the ultraviolet imaging, the visualization of the ultraviolet positioning is presented. Ultimately, the result of image fusion is provided. The positioning error evaluation of each part is carried out in the meantime. Facts proved the proposed algorithm is in high accuracy and good adaptability. Moreover, the proposed visualization method is faster locating insulation fault, and has broad application prospects.
Image Encryption using Variable Length Blocks and Variable Length PK

The colored digital image is one of the most important and popular types of digital data for use in many vital applications, which requires the provision of safe methods to protect it from penetration operations and protect it from tampering and data thieves. In this research paper, a new method for protecting digital images of various types will be presented, which is characterized by ease of implementation and providing a high degree of security and protection for the digital image. A secret color image known only by the sender and receiver will be used as an image_ key, this image_key will be used to generate a private key to encrypt-decrypted any color image by applying image resizing. The private key will be variable, and will match the image block size. The image to be encrypted-decrypted will be divided into blocks, the block size will be variable and agree upon between the sender and receiver. The proposed method will be implemented, the obtained results will be analyzed to prove the efficiency, security level and quality parameters provided by the proposed method.
Multi-task Learning Combined with RL-based Weight Search and MC Dropout

Multi-Task Learning (MTL) is a very encouraging research direction in machine learning, which has been applied in many fields successfully. However, designing a good MTL loss function and verifying the robustness of MTL model are two key challenges in this field. In this paper, we adopt RL-based weight search method to find a set of good weights in MTL loss function to help to improve the training optimization process. To improve the robustness of MTL model, especially for out-of-distribution (OoD) detection, epistemic uncertainty of MTL model is estimated via Monte Carlo dropout (MC Dropout) in this paper. Empirical studies show the proposed methods combined with MTL model is effective.
Identifying and Analyze the Face Mask Detection for The Person During Covid

In December 2019, a disease known as COVID-19 outbroke in some part of China which killed a bunch of people in that area. Till Mid-March, the disease spread all over the world killing more people. Due to this, World Health Organization (WHO) had to declare this decease as a pandemic. Therefore, scientists from all over the world were working very hard, giving their best everyday in order to make an anti-dote of this disease but it was expected to take alot of time and it even took. Now, government allowed the citizens to continue their normal way of living but still they made some protocols which was to be followed such as sanitization of hands on a regular interval, maintaining two-yard distance from each other, wearing masks, etc. Considering those protocols, we have evolved a Face-masks Detection System with a view to be useful in figuring out whether or not a person is wearing a mask or not inside the public locations which include Temples, Airports, and so on. The face masks detection database contains a mask and further to the facial snap shots, we've used OpenCV to carry out actual-time face detection from live streaming thru webcam. We have used the database to create a COVID-19 face mask detector from a computer view the use of Python, OpenCV, and Tensor Flow and Cameras. We present an in-intensity reading software that could come across conditions where a face mask isn't always used well. Our software incorporates a two-section configuration of the Convolutional Neural Network (CNN) that could hit upon hidden and unidentified faces and may point-out with pre-hooked up CCTV cameras. This allows in monitoring safety violations, promote using face mask, and ensures a safe running environment.
TrustSAMP: Securing Streaming Music Against Multivector Attacks on ARM Platform

Streaming music has dominated the digital music industry in recent years, which allows users to enjoy a huge music library online with a low subscription price. Terminal-side audio DRM (Digital Right Management) is very critical for streaming music industry, compromising of which will cause unrestricted listening, dumping and unauthorized secondary distribution. However, existing DRM protection schemes mainly focus on defeating software attacks but lack complete shielding against the physical memory disclosure attacks, which may even be launched by the owner of the terminal device. In this paper, we propose a terminal-side audio DRM solution called TrustSAMP to protect the copyrighted audio data against both software attacks and physical memory disclosure attacks. The basic idea is to process the audio data plaintext only in certain on-SoC components secured by ARM TrustZone. To minimize the TCB (Trusted Computing Base) of the secure world, we separate the control flow and the data flow of the Linux audio subsystem and port only the codes used for audio data decryption and plaintext transfer into the secure world. Moreover, we leave most driver codes of the audio-associated on-SoC components in the rich OS (i.e., in the normal world), and introduce a tiny proxy in the secure world to control the associated registers according to the requests from the normal-world drivers. The prototype implemented on real hardware shows that TrustSAMP can play a variety of wav-format audio with very small overhead and negligible loss of audio quality.
Axiological approach in the university’s transformation based on the use of digital and remote educational technologies in pandemic and normal conditions

The digitalization of education due to constant transformation is considered as an existing reality and at the same time is a challenge of a modern age. It will invariably lead to changes in the content of education, methods and organizational forms of educational work. The article considers the issues of the university’s transformation, describes the experience of using digital technologies during the mass transition to the remote studying on the example of two regional universities. Issues of axiology in transformation of education are considered.