Reviewerly/exHarmony huggingface dataset:
Application of Text Error Correction Algorithm Based on Power Inspection Voice Command Recognition

In order to ensure the accuracy of power inspection voice command recognition and promote the system to make correct actions, this paper will focus on the text error correction algorithm. Firstly, the paper introduces the status quo of voice command recognition in power inspection, and discusses the significance of text error correction algorithm. Secondly, the system is designed around the algorithm. Through this study, text error correction algorithm can accurately correct the voice command of power inspection after recognition, so as to make the command more accurate.
Algorithm Design of Dynamic Course Recommendation Model Based on Machine Learning

With the development of Internet technology, dynamic course recommendation systems based on network learning models and cloud computing have been gradually applied and developed in various disciplines. In order to improve the recommendation accuracy of dynamic courses, this paper uses the recommendation algorithm of machine learning to analyze and design. This article mainly uses experimental method and comparison method to compare and analyze the computing power of different algorithms. The experimental results show that under the two evaluation indicators of RMSE and MAE, the minimum value of NeuCNN is 1.2836 and 0.9765. These data are sufficient to show the advantages of the intelligent algorithm.
Online Examination System of Japanese Course Based on WeChat Mini Program

With the development of Internet technology and the popularity of mobile devices, the online and offline hybrid teaching mode in colleges and universities has gradually become popular. Online examination is not limited by time and place, which can effectively reduce the cost of examination and reduce the working intensity of teachers. By virtue of the advantages of the development and use of WeChat mini program, an online examination system for Japanese courses is designed and developed. The front end of the small program uses WeChat developer development tools, the back end uses Spring MVC framework, MySQL database to store business data. The system allows teachers to upload test papers and statistical scores, and students can complete online tests, exams and view scores. At the same time, the system supports automatic test paper group to avoid cheating. The system is simple to use and reliable in function. It has certain practicability for students to master Japanese knowledge points and carry out fragmented learning, and adapts to the development trend of “Internet + “ education.
The Construction of Dynamic Model of College English Blended Learning Based on Informational Data Analysis

With the development of the Internet, the education sector has realized resource sharing and information interaction, which has promoted the pace of modern education into the information age. The development of the Internet has opened up an online learning model in the education field, and has also created a foundation for the subsequent dynamic model of blended learning. In simple terms, blended learning is to combine traditional education with Internet education, and promote student-oriented teaching ideas. The dynamic mode of college English blended learning constructed in this article integrates traditional offline teaching and Internet online teaching, and analyzes the changes in students’ English scores after using this mode of teaching. From the research results, it can be seen that the failure rate of English scores and the passing rate the increase in the student’s average grade reflects a significant improvement.
Fall Detection Combining Android Accelerometer and Step Counting Virtual Sensors

INTRODUCTION: Falls constitute a significant threat to older adults. Several approaches aimed at automatically detecting falls exist. Smartphones are widespread and can serve as a low-cost pervasive platform for automated fall detection. Existing fall detection apps are highly sensitive, but often suffers from sub-optimal specificity which can result in many false positives. OBJECTIVES: The aim of this study was to investigate whether the built-in pedometer virtual sensor on the Android smartphone platform can be used to increase specificity and thereby achieve higher accuracy in an accelerometer-based Android fall detection application. METHODS: An existing open threshold-based accelerometer algorithm was combined with the standard Android virtual sensor pedometer algorithm for detecting walking in the postfall phase. In a range of experiments, falls were simulated using a combination of a test mannequin and test participants, in order to determine the sensitivity and specificity of the solution. RESULTS: All simulated falls were detected with 100% sensitivity. By counting postfall subsequent steps using the Android pedometer virtual sensor, the specificity of the application was increased to 100% in all scenarios. CONCLUSION: The combination of accelerometer and pedometer sensors was found feasible to use for increasing the specificity of existing open fall detection algorithms.
Design and Implementation of Simulated Annealing Optimization Algorithm Based on TOC

When the traditional simulated annealing algorithm solves the traveling salesman problem (TSP), with an increase in the size of the problem, the time required increases and the quality of the solution decreases. To this end, this paper takes advantage of the parallelism and bit-wise assignability of ternary optical computers and proposes a parallelization strategy that divides an optical processor into many small optical processors and then distributes the initial solution to the problem to each small optical processor. On the optical processor, the optimal solution is independently searched for. Due to the parallel search of many processors, the convergence speed of the algorithm and the global optimization ability are effectively improved. Experimental tests show that the parallel strategy can achieve a better convergence effect and effectively improve the quality of understanding.
The ghosts of old readers: social media, representation and gender in the information sector

Social media has become an almost ubiquitous method of communication and engagement, not only in the information sector but right across the increasingly digitalized world. Likewise, it has played a large role in the development of fourth-wave feminism and in movements such as #YesAllWomen, #FreeTheNipple and #MeToo, as well as in calls for improvements to the representation of gender in media. This paper draws together both aspects in order to critique and reflect upon the current usage of social media as it pertains to the representation of gender in UK university libraries, archives and special collections. It explores the challenges of utilizing such media for academic institutions deeply rooted in discourses of authority and heteronormative patriarchal power. It uses a sample of social media posts to foreground and examine a number of successes and shortcomings, with discussions informed by critical theory. In particular, foundational texts of feminist theory, such as Virginia Woolf’s A Room of One’s Own (1929) and Black Looks: Race And Representation (1992) by bell hooks, are used to frame explorations of gender and intersectionality in university collections; ultimately seeking to discover how information professionals may better represent the diverse nature of their collections on social media platforms.
Human motion recognition based on Nano-CMOS Image sensor

Human motion recognition is of great value in the fields of intelligent monitoring systems, driver assistance system, advanced human-computer interaction, human motion analysis, image and video processing. However, the current human motion recognition methods have the problem of poor recognition effect. Therefore, we propose a human motion recognition method based on Nano complementary metal oxide semiconductor (CMOS) image sensor. First, using the Nano-CMOS image sensor to transform and process the human motion image, and combines the background mixed model of pixels in the human motion image to extract the human motion features, and feature selection is conducted. Second, according to the three-dimensional scanning features of Nano-CMOS image sensor, the human joint coordinate information data is collected, the state variables of human motion are sensed by the sensor, and the human motion model is constructed according to the measurement matrix of human motions. Finally, the foreground features of human motion images are obtained by calculating the feature parameters of each motion gesture. According to the posterior conditional probability of human motion images, the recognition objective function of human motion is obtained to realize human motion recognition. The results show that the human motion recognition effect of the proposed method is good, the extraction accuracy is high, the average human motion recognition rate is 92%, the classification accuracy is high, and the recognition speed is up to 186 frames/s.
Red blood cells detection by using morphological operations at multi erosion values

Detection or differentiation of red blood cells RBCs in blood samples plays an imperative part within the field of hematology, In this study, a new algorithm was proposed to detect red blood cells from images taken of blood samples by an optical microscope by adopting digital image processing techniques depending on Morphological Operations as binary mapping, Filling holes at Multi Erosion Values. The data of DIB was used for images of blood samples and the proposed method of detection was compared with several other methods by calculating the accuracy that depends on the comparison between automatic detection and manual counting. By analyzing the results, the accuracy of the detection reached about, which is a value of 89% higher than that of the methods.
Two-Dimensional Code Information Security Design Based on Hash Function and Encryption Algorithm

With the development of information networks, encryption technology has also been improved to a large extent. In the research of information security, encryption is a very important subject. It is not only related to the integrity, confidentiality and availability of information and data. It has practical significance for the information security design of the two-dimensional code. Therefore, this article conducts related research on hash function, encryption algorithm and QR code information security, with the purpose of improving network security. This article mainly uses the experimental method and the comparative method to study the application of the hash function encryption algorithm in the information security design of the two-dimensional code. Experimental data shows that its safety performance can reach more than 90%, which basically meets actual needs.
Proposed information retrieval systems using LDA topic modeling for answer finding of COVID 19 pandemic: A brief survey of approaches and techniques

Understanding public responses to emergencies, including outbreaks of diseases, is necessary and significant. A demonstration of how to separate papers about the virus Covid-19 into different topics using topic modeling techniques in several studies is introduced in this research article. Inthe field of machine learning, topic modeling is a major topic. Though primarily, it is used to build models. It provides a quick and easy way to mine data from unstructured textual data, with samples representing documents.The most extensively utilized subject modeling approaches are Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).On the other hand, model creation can be tedious and repetitious, requiring costly and methodical sensitivity analyses to determine the ideal collection of model parameters. Moreover, comparing models frequently require time-consuming subjective opinions. The topic models assign a probability to each word in the vocabulary corpus related to one or more themes (LSA, LDA). Several LDA and LSA models with varied degrees of coherence were generated, and the model with the greatest degree of coherence was selected. This experiment demonstrates that LDA outperforms LSA.
Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos

Adversarial robustness assessment for video recognition models has raised concerns owing to their wide applications on safety-critical tasks. Compared with images, videos have much high dimension, which brings huge computational costs when generating adversarial videos. This is especially serious for the query-based black-box attacks where gradient estimation for the threat models is usually utilized, and high dimensions will lead to a large number of queries. To mitigate this issue, we propose to simultaneously eliminate the temporal and spatial redundancy within the video to achieve an effective and efficient gradient estimation on the reduced searching space, and thus query number could decrease. To implement this idea, we design the novel Adversarial spatial-temporal Focus (AstFocus) attack on videos, which performs attacks on the simultaneously focused key frames and key regions from the inter-frames and intra-frames in the video. AstFocus attack is based on the cooperative Multi-Agent Reinforcement Learning (MARL) framework. One agent is responsible for selecting key frames, and another agent is responsible for selecting key regions. These two agents are jointly trained by the common rewards received from the black-box threat models to perform a cooperative prediction. By continuously querying, the reduced searching space composed of key frames and key regions is becoming precise, and the whole query number becomes less than that on the original video. Extensive experiments on four mainstream video recognition models and three widely used action recognition datasets demonstrate that the proposed AstFocus attack outperforms the SOTA methods, which is prevenient in fooling rate, query number, time, and perturbation magnitude at the same time.
Snake Robot Motion Planning Based on Improved Depth Deterministic Policy Gradient

Due to the complexity of modular mechanism control, the motion planning of snake robots in an unfamiliar complex environment is a long-standing issue. We propose an improved deep deterministic policy gradient (DDPG) algorithm to plan the path with the shortest time and the least collisions. Traditional robot DDPG can not make full use of previous states to make decisions. This paper uses LSTM, learns all previous hidden states through memory and reasoning, and distinguishes the importance of features through the Self-Attention mechanism, which reduces the impact of useless features on decision-making and improves decision-making accuracy. In addition, the reward function is optimized to make the snake robot reach the target point faster. Finally, we do experiments in the simulation environment. The results show that the algorithm can speed up the convergence speed of the DDPG and reduce the path planning time and collision times of the snake robot.
Infrared Image Object Detection of Vehicle and Person Based on Improved YOLOv5

Existing object detection algorithms are difficult to perform object detection tasks on embedded devices under the limitations of energy efficiency ratio and power consumption due to complex network structure and huge computational and parametric quantities. The object detection task in infrared images has low recognition rate and high false alarm rate due to long distance, weak energy and low resolution. In order to achieve the detection task at the mobile edge of infrared vehicle pedestrian target detection, this paper puts the YOLOv5 algorithm into a series of optimizations and proposes a lightweight YOLO-mini network structure. That is, instead of CSPDarknet, the MobileNetV2 network structure is used as the backbone feature extraction network with the addition of coordinate attention mechanism. Also, to make the network model more lightweight, the weights are converted to int8 type by quantized sensing training, which enables the task of the object detection algorithm for infrared vehicle pedestrian dataset on embedded devices. Experiments testing the FLIR dataset on NVIDIA Xavier NX show that this algorithm greatly reduces the number of network model parameters with less loss of accuracy and improves the FPS. mAP of YOLO-MobileNetV2 reaches 86.75%, number of parameters 2.76M, and FPS of 45; The network structure of YOLO-mini achieves 84.63% mAP, 0.69M number of parameters, and 63 FPS.
Preconditioned Bminpert Algorithms for Matrix Equation &amp;lt;i&amp;gt;AX&amp;lt;/i&amp;gt; = &amp;lt;i&amp;gt;B&amp;lt;/i&amp;gt; and Their Applications in Color Image Restoration

The purpose of this paper is to show the preconditioned BMinPert algorithm and analyse the practical implementation.Then a posteriori backward error for BGMRES is given.Furthermore, we discuss their applications in color image restoration.The key differences between BMinPert and other methods such as BFGMRES-S(m, p f ), GsGMRES and BGMRES are illustrated with numerical experiments which expound the advantages of BMinPert in the presence of sensitive data with ill-condition problems.
Effectively Filtering Images for Better Multi-modal Knowledge Graph

The existing multi-modal knowledge graph construction techniques have become mature for processing text modal data, but lack effective processing methods for other modal data such as visual modal data. Therefore, the focus of multi-modal knowledge graph construction lies in image and image and text fusion processing. At present, the construction of multi-modal knowledge graph often does not filter the image quality, and there are noises and similar repetitive images in the image set. To solve this problem, this paper studies the quality control and screening of images in the construction process of multi-modal knowledge graph, and proposes an image refining framework of multi-modal knowledge graph, which is divided into three modules. The final experiment proves that this framework can provide higher quality images for multi-modal knowledge graphs, and in the benchmark task of multi-modal entity alignment, the effect of entity alignment based on the multi-modal knowledge graphs constructed in this paper has been improved compared with previous models.
Artificial Neural Network Design for CMOS NAND Gate Using Sigmoid Function

Artificial Neural Network (ANN) is very useful to predict the future. These predictions can be done in the area of agriculture, transport, finance, health care, etc. Complementary Metal Oxide Semiconductor (CMOS) circuits are used for the design of the hardware. In this paper, we are taking CMOS NAND circuit and experimenting how the NAND gate is useful for an intelligent system. The proposed work was done using the sigmoid function for observing the activation function of NAND gate. The results shown are promising and viable in practical applications.
Novel Design of Quantum Circuits for Representation of Grayscale Images

The advent of quantum computing has influenced researchers around the world to solve multitudes of computational problems with this promising technology. Feasibility of solutions for computational problems, and representation of various information, may allow quantum computing to replace classical computer in near future. One such challenge is the representation of digital images in quantum computer. Several works have been done to make it possible. One such promising technique, named Quantum Probability Image Encoding (Yao in (Phys Rev X 7(3):031–041) [1], Quantum Edge Detection—QHED Algorithm on Small and Large Images [2]), requires minimal number of qubits, where the intensity of n pixels is represented as the statevector of $$\left\lceil {\log_2 n} \right\rceil$$ qubits. Though there exist quantum circuit design techniques to obtain arbitrary statevector, they consider statevector in general Hilbert space. But for image data, considering only real vector space is sufficient that may constraint the circuit in smaller gate set, and possibly can reduce number of gates required. In this paper, construction of such quantum circuits has been proposed.
A Multiscale Aligned Video Super-Resolution Network for Improving Vibration Signal Measurement Accuracy

The quality of sequence frames is the premise of obtaining accurate vibration curves by visual vibration measurement method. Existing methods for video super-resolution reconstruction (VSR) have large models and slow inference speeds, which are not suitable for practical industrial applications. This paper proposes a video super-resolution network framework based on the pyramid and deformable convolution to measure the three key indicators of algorithm accuracy, speed, and model size. Specifically, we designed a pyramid alignment module (PAM) that includes a deformable self-attention module (DSA) to focus on the motion estimation of the region of interest while suppressing unrealistic motion estimation. In addition, the two-way propagation method combined with the PAM can be used to ensure that all frames in the input sequence can be balanced to enjoy the information of the full sequence of frames when aligned. The experimental results show that the proposed method improves the PSNR by 0.58dB compared with the existing reconstruction algorithm on the high-speed rotor image data set produced in this paper, and reduces the reasoning time by 41.5% and the model size by 9.1%. Finally, the rotor vibration displacement analysis experiment shows that the vibration signal obtained from the rotor data enhanced by this algorithm has better periodicity and stability. Our code will be available at https://github.com/candygogogogo/Rotor-Sequence-Image-Enhancement.
Hiding speech in video using swarm optimization and data mining

In a digital environment, secure data transfer is not possible via an unsecured medium due to intruders or hackers who manipulate data in an unauthorized manner. Data can be transmitted safely by using data hiding methods to prevent data leakage. Both data hiding and data encryption are used in this work to give dual safety. A speech signal file (.wav) has been invisible after being encrypted in a video file (.avi) through three stages. The first stage involved scrambling bits using a Brownian motion algorithm, scrambling blocks using a circle map algorithm, and finally shuffling the speech signal as a whole. The second stage involved encrypting the scrambled speech signal file with the ABC optimization algorithm. In the third stage, we used the k-means and least significant bit (LSB) algorithms for hiding.The paper results were very successful in encrypting a speech signal file that was difficult to recognize. That has been made possible by employing a multi-level scrambling approach. The measurements SNR, SNRseg, CC, LLR, PSNR, MSE, SSIM, and PRD are used to evaluate the ability of the encryption and hidden strategies. The steganography result has well relative to the difficulties of hiding the major speech signal file after encrypting and retrieving it without altering its contents.
An Extensive Survey on Sentiment Analysis and Opinion Mining: A Software Engineering Perspective

Context—The authors have analyzed the opinion mining and sentiments related to software Engineering and what are the sentimental issues software engineers are facing in the current scenario. Objective—The authors have obtained the overall solutions to research issue and finding what are the research challenges and gaps related to sentiments and opinion. Conclusion—The authors of current paper, have analyzed the work done in various research papers on sentimental analysis related to software engineering. In software engineering process, the authors include a process where authors analyze and classify the positive, negative and neutral polarities of the opinions and reviews. This process is called sentiment analysis in software engineering. The authors give systematic and extensive survey on sentiment analysis and opinion mining.