Reviewerly/exHarmony huggingface dataset:
An Enhanced Voice Assistance using Recurrent Neural Network

The preceding decade has brought huge development in voice assistants. The speech recognition system along with cognitive and linguistic system are interdisciplinary areas that contribute to the field of speech construction and auditory observation. This study aims at developing a speech recognition system with the help of Recurrence Neural Network (RNN), a deep learning model for identifying the voice signals. This mechanism reduces the use of input devices and hardly requires more knowledge on feature selection. The hidden layers monitor the time sequence of audio signals between the transformation from one layer to another. The word error rate is the metric used to evaluate the efficiency of the model based on the number pf epochs and the input size.
3D Body Sensor-Based Martial Arts Demonstration and Teaching System

The existing human action recognition methods have imperfect models, resulting in low recognition accuracy, lack of famous teacher resources, shortage of judges, and strong subjectivity in competition and test scoring. Based on Kinect 2.0, through Unity3D program development and 3D character model development, this system designs innovative continuous action algorithms ( mainly including segmented depth search method and weighted matching method), and realizes Chinese martial arts action demonstration, standard action entry, martial arts action scoring and other functions
Software Vulnerabilities Detection in Agile Process using graph method and Deep Neural Network

Software development is rapidly growing at a global level. It requires a lot of technical knowledge and a well-structured skill set. Due to these and other factors, software development projects contain elements of uncertainty. Risks can also be defined as the possibility of the occurrence of an event which may either have a positive or negative impact on the result. Risk management is one of the most important functions of management strategies. In project management, risk management plays an important role in preventing and mitigating risks that have the potential to adversely affect the specified outcomes. Artificial neural network-based training algorithm is used to predict the risks in agile software development project. They help project managers in decision making and predicting the risks involved in the projects with respect to the resources, techniques and other constraints involved.
Blood Cell Detection and Counting via Deep Learning

A vital component of clinical medical diagnosis is blood cell count. CNN has devised an effective way of automatically counting blood cells using deep learning-based detection method. Inadequate bounding box alignment and overlapping item recognition are challenges for the CNN detection approach. We suggest a brand-new deep-learning technique called CNN to get over these restrictions. Channel, spatial attention mechanism is incorporated into the feature extraction network resulting in CNN. For residual fusion, CNN can assist the network in increasing detection accuracy by replacing the original feature vector and employing the filtered and weighted feature vector. The experimental results show that the typical CNN network may improve blood cell count detection performance without adding too many extra parameters, where the accuracy of identifying cells (RBCs, WBCs, and platelets) has been done.
Security and Privacy Preserving in Big Data

The area of large collection of Information is developing at an upsetting rate. The extreme usage of Person-to-person communication Locales, combination of Information from Sensors for assessment and assumption for future events, improvement in Consumer loyalty on Web based Shopping entrances by observing their previous way of behaving and giving them data, things and offers of their advantage momentarily, and so on had prompted this ascent in the field of Big Data. Security of Information and Protection of Client is of particular interest and high significance for people, industry and the scholarly world. Everybody guarantee that their Sensitive data should be avoided unapproved access and their resources should be remained careful from security breaks. Protection and Security are likewise similarly significant for Huge Information and here, ensuring the Protection and Security is common and complex, as how much information is colossal. One potential choice to actually and proficiently handle, process and dissected the Large Information is to use AI techniques. Simulated intelligence strategies are clear; applying them on Large Information requires objective of various issues and is a troublesome endeavor, as the size of Information is as well enormous. The proposed work is connected with further develop protection and security issues and hazard at various phases of Big Data.
Design and practice of "Internet +" blended teaching

The advent of the "Internet +" era has made profound changes in the field of education. With the development of education informationzation, various teaching platforms and mobile terminals are timely, providing technical support for the transformation from traditional teaching to Blended teaching. This paper takes ketang-pai as the tool of intelligent teaching, takes financial marketing course as an example, studies the new mode of hybrid teaching, and puts forward suggestions to optimize the teaching effect based on practice.
Multi-view Graph Convolution for Dialogue State Tracking

Dialogue state tracking (DST) is a significant part of prevalent task-oriented dialogue systems, which monitors the user’s goals based on current and previous dialogues to achieve effective dialogue management. However, the information sharing among domain-specific slots is a main challenge in DST design, especially in terms of information interaction among domain-specific slots. To deal with this challenge, multi-view graph convolution is introduced in this paper to help DST models learn domain-specific associations among slots. A multi-view graph is presented to provide more associative information for the embedding of each domain-specific slot. Compared with a single-view graph, which can only learn global domain-specific slot associations, the multi-view one incorporates two additional graphs for learning similar and non-similar domain-specific slot associations, thus improving the inter-slot information sharing capability of DST models. Experiments results shows that the proposed method achieved a higher joint goal accuracy than that of existing state-of-the-art DST models.
Interaction through Computer Vision Air Canvas

The material and presenting it on the screen using the application is a part of the interaction that is possible through the computer vision air canvas. Having the various colours present is also a part of this interaction. The varied colour schemes make it easier for the user to identify things and provide greater clarity. Accessing the built-in web camera on the laptop or the independent web camera that was installed is required to accomplish this. This contributes to a better overall knowledge and provides the user with a more concise description of the air. In addition to that, this is utilised for text visualisation and drawing for the audience. This has the potential to serve as a stepping stone for more innovative streams and material that is engaging in the future. Simply moving your finger through the air will allow you to draw your creative ideas, which does make use of computer vision technology. In the respective paper, we construct a screen through which the information or text that we draw by waving is displayed appropriately on the screen for which is done by employing shooting the motion of finger using internet digital camera. This is accomplished in a manner similar to how a touch screen works. The detection of the colours, tracking of the marker, and establishment of the coordinates are the objectives of this particular piece of writing.
Task Allocation in Containerized Cloud Computing Environment

Containerization technology makes use of operating system-level virtualization to pack application that runs with required libraries and is isolated from other processes on the same host. The lightweight easy deployment of containers made them popular at many data centers. It has captured the market of virtual machines and emerged as lightweight technology that offers better microservices support. Many organizations are widely deploying container technology for handling their diverse and unexpected workload derived from modern applications such as Edge/ Fog computing, Big Data, and IoT in either proprietary clusters or public, private cloud data centers. In the cloud computing environment, scheduling plays a pivotal role. In the same way in container technology, scheduling also plays a critical role in achieving the optimum utilization of available resources. Designing an efficient scheduler is itself a challenging task. The challenges arise from various aspects like the diversity of computing resources and maintaining fairness among numerous tenants, sharing resources with each other as per their requirements, unexpected variation in resource demands and heterogeneity of jobs, etc. This survey provides a multi-perspective overview of container scheduling. Here, we have organized the container scheduling problem into four categories based on the type of optimization algorithm applied to get the linear programming Modeling, heuristic, meta-heuristic, machine learning, and artificial intelligence-based mathematical model. In the previous research work has been done on either Virtual machine placements to Physical Machines or Container instances to Physical machines. This leads to either underutilized PMs or over-utilized PMs. But in this paper, we try to combine both virtualization technology Containers as well as VMs. The primary aim is to optimize resource utilization in terms of CPU time. in this paper, we proposed a meta-heuristics algorithm named Sorted Task-based allocation. Simulation results show that the proposed Sorted TBA algorithm performs better than the Random and Unsorted TBA algorithms.
Multi-level feature learning approaches for video recommendation

This paper addresses to assess the relevant visual strength between two videos based on a great deal with image content analysis. After custom pre-trained image and video content using multi-level feature learning model, video features are widely applied to image and video representation. Although, certain features are task-specific, two videos cannot be the best for all types of work. Additionally, for various reasons like ownership, including anonymity, people only have access to predetermined video functions. Refined video features can be reused without returning to the original video information. For example, an affine transformation is accomplished by reimagining a known function into a new space. We proposed to use maximizing the re-learning method for video recommendation. Instead of creating more training data, we suggested a modern data enhancement approach for a frame-by-frame and video-by-video basis task. Extensive testing of our proposed model is considered using real time data set and found the efficacy of the process and lends strong proof to the performance of video recommendation.
Design of improved adaptive Kalman filter based on data fusion

This paper designs a new adaptive Kalman filter (KF) to solve the filtering problem of with unknown noise statistics. The estimation error of the process noise covariance matrix will destroy the state estimation, and the state estimation error will reduce the estimation of the process noise covariance matrix, thus affecting the stability and optimality of the filter. In addition, most adaptive KF algorithms adopt the measurement method of single sensor, which is easy to cause the shortage of measurement data and affect the accuracy of the algorithm. Therefore, we propose a measurement sequence fusion adaptive Kalman filter (MSFAKF) algorithm to solve this series of problems. Based on MSFAKF algorithm, the estimation of process noise covariance and measurement noise covariance are obtained by measurement fusion sequence and innovation sequence respectively. Under certain conditions, the stability of MSFAKF algorithm is proved, and the filtering result converges to that of ideal KF with known second moment of noise. Simulation experiment shows that MSFAKF algorithm has the characteristics of fast convergence speed, high accuracy and strong adaptability.
Human Pose Estimation Using GNN

Human Pose Estimation is a method of capturing a collection of coordinates for each joint (arm, head, torso, etc.) that may be used to characterize a person's pose. The initial goal is to create a skeleton-like depiction of a human body, which will then be processed for task-specific applications. The ability to identify and estimate the position of a human body is valuable in a wide range of applications and conditions like action recognition, animation, gaming, and so on. It is a crucial first step toward understanding people through images and media. In this study, graph neural networks were utilised to predict human poses by modelling the human skeleton as an unordered list, greatly enhancing 3D human pose estimation. This paper describes the approach as an efficient way to determine the 3D posture of many persons in a picture. Our model gives a validation accuracy of 92%.
Threat Recognition Through Victim And Assailant's Pose And Used Threat Object By Applying YOLOv5s Algorithm

This study aimed to recognize threats by recognizing the assailant pose, victim pose, and the threat object used by the assailant in one frame in a threat emergency situation using a 2D camera and by applying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$YOLOv5s$</tex> algorithm. The system's ability to correctly identify threats depends heavily on the training and labeling in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$YOLOv5s$</tex> . Thus, the bounding boxes were carefully assigned, and the labels were arranged properly. Through the application of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$YOLOv5s$</tex> algorithm, supervised learning was implemented. Recognized threats were identified by recognizing the three variables including, victim pose, assailant pose, and threat object in one frame. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$YOLOv5s$</tex> were able to localize the pose and object and avoid misclassification by setting the appropriate Intersection over Union (IoU) and confidence threshold. Using a truth table, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$YOLOv5s$</tex> was able to identify threats by removing possibilities that were not even threats. As for the result, the system was able to recognize each of the assailant poses, victim poses, and threat objects in one frame. Thus, the system was able to obtain an overall reliability of 98.125%.
Medical Image Segmentation Using Deep Learning

The classification of medical imaging is that specialists and radiologists stick to the end of the disorder. Basic studies based on convolutional cerebrum relationships (CNNs) are used to aid flexibility at the end of the clinic. Three systems are considered to distinguish affected tissues. CNN contextually identifies every single pixel of the image as an a location that is both intriguing and uninteresting. RoI is then used to separate the impacted area. The second method removes pixel position information from image data using scalable and improved techniques (autoencoders). The non-convolutional layer separates geographic information associated with opposing features and also forgets to retrieve important ward information for prominent components of the level. In the third structure, the U-Net thought module receives the relevant ward information. Channel size, read rate, and k-crease section verification were adjusted to break the membrane similarity coefficient (DSC).
A hybrid deep learning based robust framework for cattle identification

This study proposes a deep learning-based framework for recognizing cows based on images of their muzzle, and faces. This method works well when dealing with missing or false insurance claims. This study proposes a hybrid multi-stage framework consisting of different phases such as augmentation, denoising, enhancement, and classification. The proposed framework is developed by hybridizing convolutional denoising autoencoders (CDAE), least squares generative adversarial network (LS-GAN), Xception feature extractor, and a convolutional neural network (CNN). CDAE is used to initiate the process of denoising noisy images. LS-GAN is used to improve the characteristics of denoised images by enhancing the image by elimination of the residual noise. The Xception is utilised to extract significant and optimal features, and CNN is then used for classification. Various comparative methodologies are used to assess the proposed approach at different phases through several statistical measures. The proposed framework achieved 97.27% accuracy using the test datasets, which is higher than the comparative approaches.
Performance Analysis of Feature Selection Techniques in Software Defect Prediction using Machine Learning

Software Testing is an essential activity in the development process of a software product. A defect-free software is the need of the hour. Identifying the defects as early as possible is critical to avoid any disastrous consequences in the later stages of development. Software Defect Prediction (SDP) is a process of early identification of defect-prone modules. Lately, software defect prediction coupled with machine learning techniques has gained momentum as it significantly brings down maintenance costs. Feature selection (FS) plays a very significant role in a defect prediction model's efficiency; hence, choosing a suitable FS method is challenging when building a defect prediction model. This paper evaluates six filter-based FS techniques, four wrapper-based FS techniques, and two embedded FS techniques using four supervised learning classifiers over six NASA datasets from the PROMISE repository. The experimental results strengthened that FS techniques significantly improve the model's predictive performance. From our experimental data, we concluded that SVM based defect prediction model showed the best performance among all other studied models. We also observed that Fisher's score, a filter-based FS technique, outperformed all other FS techniques studied in this work.
Online Automobile Rental and E-Marketplace with Augmented Reality (AR)

The major goal of this research article is to alleviate the suffering of people who need a temporary vehicle or are looking to purchase a new vehicle. Those who do not possess a car, owners of damaged or destroyed automobiles awaiting repair or insurance settlement, or out-of-town travelers, for example. The technology of augmented reality can be utilized to enhance user experience in the process of purchasing new automobiles. Also, being able to add their own cars to put up for sale, can be rewarding to users who want to get rid of their old vehicle easily.
Moving Object Detection using Background Subtraction

Moving article recognition is an eminent phase of the video treatment. To identify a moving object, we put picture deduction calculations into practice. With the help of MATLAB programming, we calculated the distance, outline per time, and speed. In traffic light observation systems, this can be used to estimate speed. For spotting moving objects in recordings from stationary cameras, background removal is a common technique. The approach's justification is that it can identify moving objects by comparing the change between the current frame and a reference frame, often known as a background image or background model.
Improved YOLOv4 Based on Transformer and Feature Fusion Module

In order to solve the problem that mainstream object detection models such as R-CNN, YOLO and SSD focus too much on network depth and neglect the fusion of deep semantic feature information with shallow layers, this paper proposes a new network model based on transformer and feature fusion module, named as YOLOv4-RFEM and FGFM(YOLOv4-RF). YOLOv4-RF can show better results in the use and fusion of multi-scale feature maps. Firstly, to solve the spatial pyramid pooling layer used in YOLOv4 is deficient in integrating global information, we propose a Receptive Field Enhancement Module, named RFEM, based on the idea of transformer. RFEM can better handle the global information and enhance the feature map to achieve higher detection accuracy. Secondly, a Feature-Guided Fusion Module (FGFM) is designed to solve the problem of inadequate fusion between different feature maps. By introducing channel attention, different feature maps are fully fused to enhance network performance. Our experiments on the PASCAL VOC2007+2012 dataset achieved the mAP of 91.55%. The experimental results show that the model can improve the accuracy of object detection.
A Study on Sentiment Analysis of Online Course Review Information Based on XLNet-BiGRU

Texts from course comments play a crucial role in the online learning environment on the Internet. In a positive way, the sentiment data contributes significantly to classroom engagement, enhances the effectiveness of instruction, and fosters the long-term growth of online courses. How to effectively assess the emotional qualities of Chinese course review information and adjust the course quality properly is one of the hot topics of the current study. In this study, we propose a sentiment analysis model based on XLNet-BiGRU for categorizing the sentiment polarity of course review text in Chinese online courses. First, the text corpus data is learned using the XLNet pre-training model. The major sentiment characteristics and contextual features are then extracted from the comments using the BiGRU model, and the attention mechanism is used as a weighting method to increase the weight of the sentiment words in the sentences. The Softmax layer ultimately classifies the weighted features. According to the experimental findings, the XLNet-BiGRU model, which incorporates the BiGRU model, can fully extract the text’s deep semantic information and, to a certain extent, increase the task’s effectiveness in classifying sentiments.
Coupled Local and Global Semantic Alignment for Image-Text Matching

The regional features in the image tend to focus more on the foreground information of the image, and the global features in the image tend to focus more on the important background information in the image. How to effectively integrate the foreground information and background information in the image have become an urgent problem to be solved. To address the above issus, we propose a novel Coupled Local and Global Semantic Alignment network. First we propose global image features of images to preserve important background information in images. Second we extract regional image features to preserve fine-grained semantic information in images. Then we utilize the GRU network to extract features for each word in the text. We align image features and text features globally and locally to obtain global similarity and local similarity. We assign different similarity weights to the similarity matrix to aggregate the similarity matrix. Extensive experimental results demonstrate the effectiveness of our proposed method.