Reviewerly/exHarmony huggingface dataset:
Background Buster: Peeking through Virtual Backgrounds in Online Video Calls

Video calling applications such as Zoom and Skype have become the preferred medium for both personal and professional communications. One feature in these applications that has gained prominence is the virtual background feature, which enables users to conceal their background by blending in a virtual image or video in place of the real background, thus providing users with background and contextual privacy. However, this feature is not robust enough, and depending on the target user’s activities, movement and accessories worn during the call, portions of the user’s background could leak which can then be reconstructed to reveal significant portions of the user’s real background, and other contextual information related to the real background. This paper conducts an investigative analysis of the background privacy provided by the virtual background feature in video calling applications by designing a novel background reconstruction framework, and using it to reveal users’ real background. By means a large dataset of call videos, collected from human subject participants and in the wild, a comprehensive evaluation of the proposed framework and related privacy attacks under a variety of different experimental parameters is then carried out. Results from these evaluations show that significant leakage of background information is feasible under certain conditions, rendering the feature ineffective in protecting privacy and giving users a false sense of security.
Image Quality Assessment by Integration of Low-level &amp; High-Level Features: Threshold Similarity Index

The increase in the implementation of deep learning models in image processing has shifted its focus towards better-performing models within the ambit, directly proportional to the size of the dataset. A common approach to increasing the available data for training is via data augmentation. Additional data is created either by modifying gathered data or creating synthetic data based on the seed dataset. This article presents an approach to validating synthetic images generated by a Deep Convolutional Generative Adversarial Network (DCGAN) using full-reference image quality assessment techniques. We have presented an algorithm based on the Threshold Similarity Index(TSI) to validate synthetically generated images. The TSI becomes a special index. TSI calculation has been done by amalgamating high-level features like luminance, contrast, structure, and low-level features like edges and zero crossings of the correlated images. TSI becomes an integrated matrix of our algorithm by incorporating the features of SSIM and FSIM both. The developed algorithm has been verified by generating synthetic images using the DCGAN model, which generated 35 images. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{TSI}_{FSIM}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{TSI}_{SSIM}$</tex> of this model were calculated to be 0.637 and 0.127 respectively. The proposed algorithm validated 85,7% of synthetically generated images to be included in the seed dataset.
Analysis and Research on the Signal Quality of the Beidou-3 Signal with EWS

With the upgrade and fast construction of the BeiDou Navigation Satellite System (BDS), the innovation of the navigation signal system has already entered a stage of fast developing. The quality of satellite signals directly impacts the performance of satellite navigation system. Navigation signal quality analysis and research are effective methods to ensure the navigation performance. Therefore, it is of great theoretical and application significance to research the methods for signal quality evaluation. This paper introduced the B2a signal of BeiDou-3 quality assessment parameters and the measurement methods, discussed the power spectrum density, eye diagram, constellation, correlation loss and s-curve bias of COMPASS navigation signals. Conclusion could be drawn through the comparisons of the test results and ideal values.
Research on Drilling and Completion Design Knowledge Base System based on Knowledge Map

The continuous development of knowledge atlas technology provides a richer implementation method for drilling and completion design knowledge base. In order to promote the standardization of data management, the dominance of knowledge management and the maximization of data value and realize the asset management and knowledge appreciation of drilling and completion design data, aiming at the existing problems and resource utilization of drilling and completion design, various technologies based on knowledge map and intelligent search are proposed to build the drilling and completion design knowledge base system. The system focuses on the construction of drilling and completion design knowledge system and knowledge atlas, knowledge base construction and knowledge application, extracts the key information in scientific research achievements, automatically labels and classifies the achievements, improves the standardization of data management and the utilization rate of data mining, is conducive to improving the efficiency of scientific research and promoting the high-quality development of drilling and completion design.
Probabilistic foundations for machine learning

The subject of likelihood is the subject of probability theory, which is a branch of mathematics. Probability theory is the mathematical foundation of statistical reasoning, and it is critical for data scientists to understand how unpredictability affects data. It is crucial in machine learning since probabilistic data assumptions are regularly used in the construction of learning algorithms. The idea of probability is used to quantify the degree of uncertainty. The purpose of probability theory is to use a set of axioms to express uncertain phenomena. To cut a long story short, we try to describe the situation by estimating the likelihood of various occurrences and scenarios when we cannot be positive of the system's likely outcomes. This article will cover random variables, independence, entropy, and the Chebyshev inequality. These mathematical theorems and notions are necessary and vital for future research in other fields of science.
Target Detection Algorithm of Intelligent Shared Car under the Background of Blockchain Technology

Intelligent sharing technology is one of the trends of future development. Based on the traditional automobile industry, the intelligent sharing vehicle researched by intelligent sharing technology will be welcomed by people, and the problem of vehicle target detection based on blockchain technology will also be improved. The disadvantages of smart sharing. Based on the very popular blockchain, this paper studies the target detection algorithm of intelligent shared vehicles, solves the problems existing in the traditional automobile industry, and further introduces intelligent sharing technology to explore, solve the problems existing in the industry, and realize the realization of people's lives of intelligence. This experiment is to solve the problems of the traditional automobile industry, using blockchain technology to study the intelligent shared car target detection algorithm, when users use the intelligent shared car, they can get good service, and they can evaluate and store each other after completing the driving on the blockchain. The experimental results show that in 2021, the number of car rental companies nationwide will exceed 10,000, and according to market demand, the annual growth rate will be about 30%, and the intelligent car sharing industry is still in its infancy, so it is relatively easy to use blockchain technology for research. Less, so the intelligent shared car industry is a very promising industry in the future.
Development and Comparative Analysis of Event Relation Extraction Methods

Event relation extraction is an important research direction in the field of information extraction. Compared with named entity recognition, entity relation extraction, and event extraction. Event relation extraction can extract common-sense knowledge that is more in line with human intuition from unstructured texts and can be organized together in the form of logical interaction. In this paper, the current event relation extraction works are reviewed in detail. This paper is organized according to different types of event relations. In addition, this paper also introduces two kinds of event relation data sets. Finally, we discuss the challenges and prospects of event relation extraction.
Target search path planning for mobile robot under fixed time

In the target search problem of mobile robot in uncertain environment, the probability of target discovery at the observation point is often set as an ideal uniform distribution, and its path optimization index is usually the shortest distance, but the shortest distance path is not equal to the optimal expected time path To solve this problem, a probabilistic multi-objective search algorithm based on expected time is proposed in this paper In view of the phenomenon that different access order of observation points will lead to different expected time, a hierarchical path optimization strategy is adopted Firstly, a new model for calculating the probability of non-uniform target distribution is constructed; Then, in the upper level sequence planning, the improved improved circle algorithm is used to generate the expected observation sequence; Finally, in the feasible path planning between observation points of the lower feature map, the improved fast random spanning tree algorithm (GBC - RRT) is used The experimental results show that the proposed method can significantly shorten the expected time of mobile robot target search, and can obtain the search path of the optimal expected time in the workspace with uncertain and non-uniform distribution of targets.
Design of Nanchang VI Visual Image Recognition System based on Artificial Intelligence

With the development of modern cities, more and more reinforced concrete buildings appear around us. This article mainly studies the design of Nanchang vi visual image recognition system based on artificial intelligence. This paper makes systematic and standard design and application design research on the concept identification system, basic design system, and application design system of Nanchang's characteristic industrial city image identification design system, especially for office, advertising, light box, and guidance in application design. The survey in this article shows that in terms of social services, Nanchang believes that the convenience of credit card services accounts for 71.4%, the convenience of telephone and Internet reaches 67.2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , the convenience of consumer shopping is 77.1%, and there are many public playgrounds such as parks and supporting facilities. For 74.2% and 63.3%, it can be seen that students' recognition of Nanchang's social services is still very high.
Super resolution reconstruction of single image based on Res-Net and Sub-Pixel

How to improve the resolution of image is a classical problem in the field of image processing. In recent years, methods based on Convolution Neural Networks (CNN) or deep neural networks have made huge success. Researchers use a single filter behind the input layer or before the output layer to upscale Low resolution (LR) image to be a High resolution (HR) one. Moreover, too deep layers are used to capture the feature maps of the LR image, which will make the network be much more time consuming. In our work, we proposed a new approach which involves three convolution layer and two Residual Network (Res-Net) and a Sub-pixel mapping layer for the reconstruction. On the one hand, two shallow Residual Network can protect the integrity of the information of LR image to a certain extent by transferring the input information directly to the output of the next convolution layer. On the other hand Sub-pixel mapping layer which can learn complex filters to upscale the final layer of LR feature maps to be HR image. The experiments are made based on images of publicly datasets and the results as shown in this paper that our approach performs better than previous CNN-based methods, meanwhile it can perform in a real time.
Visual Object Tracking: Method and Comparison

Visual Tracking is an important branch of computer vision, it begins with a frame, identify target objects from different backgrounds, modeling target's appearance, analysis and modeling target's motion information in the subsequent frames, so as to continuously predict the motion state of the target, and determine its position in each frame of image, and finally realize the tracking of an object. In recent years, many excellent and classic algorithms and frameworks have emerged in this field, this is divided into two main categories, which are correlation filtering methods and deep learning methods. They have their own advantages. For example, excellent correlation filters can achieve high-speed and stable tracking effects. With the help of deep learning networks, target features can be obtained in advance to achieve more accurate tracking. In this paper, we have summarized 15 papers covering four directions or fields of Correlation Filter, Siamese Network, Multi-domain Network and Transformer. The summary includes 2 frameworks and 13 algorithms or methods. We also analyzed the advantages and disadvantages of the summarized algorithms, and gave a performance comparison between different algorithms.
Research Status of Speech Driven Virtual Face Animation Methods

In recent years, the field of virtual reality has developed by leaps and bounds. As an important intelligent technology in the process of virtual life, voice driven virtual face animation can use a piece of voice to create real and natural face animation of virtual characters. It is widely used in the fields of intelligence education, virtual anchor, digital entertainment and so on, which meets the needs of human-computer interaction in the field of virtual reality. In order to further promote the technological breakthrough and intelligence in the process of virtual face animation generation, this paper compares and analyzes the key problems of voice driven virtual face animation, combs and summarizes the core technologies such as voice visual mapping, face model construction and virtual face animation generation, and looks forward to the future research and development direction of voice driven face animation, It is of great significance to the further research and development of speech driven facial animation.
A new mechanical ecological floating island based on refrigerant and big data

This project aims to improve the ecological floating island in view of the problems of low artificial maintenance cost, poor wind and wave resistance of floating islands, rotten roots of plants and difficulty in adapting to the climate environment. By studying refrigerant materials, carbon fiber heating, and air-cooling technology, we aim to cope with changes in ambient temperature and maintain the temperature of plant plates. By combining big data and electronic temperature detection to achieve ambient temperature control of different types of plants, in addition to the research and design of reasonable mechanical structure to achieve the improvement of floating island resistance to wind and wave, and mechanized to achieve the purpose of root rot prevention. Finally, a new type of ecological floating island with no large labor cost investment, high degree of automation, longer service life, higher environmental purification efficiency, and combined with big data is designed through computer-aided design
Research on the Frameworks of Visual Tracking

With the development of visual tracking, the visual tracking plays a significant role in various fileds: traffic control, surveillance, facial recognition and so on. In this paper, I decide to introduce three kinds of framework about the Transformer Tracking, MDNet and correlation filter. At the same time, I will find out the characteristic of these three frameworks. By comparing each of these frameworks, I will analyze the advantages and disadvantages of these frameworks. I also provide the in-depth analysis on each framework and provide a brief guidance on how to select them. We note that our paper can provide an introduction of visual object tracking for those beginners.
Research on Image Encryption based on Generalized M-J Set

With the rapid development of information technology, hacker invasion, Internet fraud and privacy disclosure and other events frequently occur, therefore information security issues become the focus of attention. Protecting the secure transmission of information has become a hot topic in today's research. As the carrier of information, image has the characteristics of vivid image and large amount of information. It has become an indispensable part of people's communication. In this paper, we proposed the key simulation analysis research based on M-J set. The research uses a complex iterative mapping to construct M set. On the basis of the constructed M set, the constructed Julia set is used to form the encryption key. The experimental results show that the generalized M-set has the characteristics of chaotic characteristic and initial value sensitivity, and the complex mapping greatly exaggerates the key space. The research on the key space based on the generalized M-J set is helpful to improve the effect of image encryption.
Intelligent Integrated Sewage Treatment and Magnetic Powder Recovery Device based on Halbach Permanent Magnet Army

In view of the complicated process of sewage treatment and magnetic flocculation technology, an integrated device for magnetic particle separation and sludge precipitation is designed, which simplifies the sewage treatment process and achieves the purpose of saving electricity and improving sewage treatment efficiency. The optimization of magnetic flocculation technology and energy saving and emission reduction are realized. This project is to realize the process of flocculation sedimentation and magnetic particle separation at the same time. Based on the Halbach array, the magnetic roller is optimally designed, and the cylinder structure is cleverly designed. The intelligent control ball valve collects sludge, and the construction of the designed integrated facility has great social significance.
Research on the Construction of Personalized Intelligent Recommendation Platform for National Reading Empowered by Blockchain

Combining the prominent features of the blockchain and leveraging the key technologies of the blockchain to empower the construction of a nationwide reading recommendation platform, it is of great significance to actively promote the integrated development of the blockchain and the economy and society, and comprehensively promote the high-quality development of national reading in the new era. The construction of a national reading recommendation platform empowered by blockchain technology mainly includes four subsystems: user management, resource management, smart reading and reading evaluation. Transforming into a “demand-determining service” model, providing personalized and precise services based on reading traces, collection preferences, etc., on the basis of safety, openness, cooperation, and order, providing a good ecological cycle for continuous reading, continuous creation, and lifelong learning for the whole people system.
Research on the Application of Computer Big Data Technology in English Multimedia Teaching

This paper first analyzes the working principle of the distance teaching system for English teaching classrooms, constructs a three-tier architecture of the distance teaching system for English teaching classrooms, then designs the system based on Mutagen Creator 3.2, and uses the core control module to realize the output of remote multimedia teaching information. Control, and use 3DSMAX virtual technology to create a virtual teaching environment, realize the interaction between scene objects and users, and show the teaching effect smoothly. Teaching effectiveness and system performance is very stable.
Design of hospital case information retrieval system based on improved AES algorithm

Due to the relatively large demand for hospital case information retrieval and the high probability of concurrent requests, the information security is greatly threatened. Therefore, the design of hospital case information retrieval system based on improved AES algorithm is proposed. Take the ARK-1123h processor and ADK3390-DH21 power supply device that can meet the multi task Internet information processing as the hardware of the system. After normalizing the case information by using the linear function conversion, the gradient parameters are integrated into the AES algorithm to encrypt the information, and the corresponding information is displayed in combination with the user identity authentication results. The test results show that the designed system can effectively ensure the security of hospital case information under the condition of concurrent access.
Biometric Identification System based on Object Interactions in Internet of Things Environments

Attributing interactions with Internet of Things (IoT) devices to specific users in smart environments is extremely important as it enables personalized configurations and access control. This requirement is particularly stringent when it comes to parental control measures designed to protect children from contact with dangerous machinery or viewing materials that are inappropriate for their age. To this end, we show that naturally occurring interactions with objects in smart environments can be used as a behavioral biometric in order to identify users. The heterogeneous nature of smart devices enables the collection of a wide variety of inputs from such interactions. In addition, this system model allows for seamless identification, without the need for active user participation or rearrangement of the IoT devices.We conduct a remote study taking place in six households composed of 25 participants. We demonstrate that our system can identify users in multi-user environments with an average accuracy of at least 91% for a single object interaction without requiring any sensors on the object itself. This accuracy rises to 100% when six or more consecutive interactions are considered.
Research Report: On the Feasibility of Retrofitting Operating Systems with Generated Protocol Parsers

Parser generators show great promise in producing more secure input validation and processing routines. Operating system kernels are a particularly appealing place to deploy generated parsers due to their position at the periphery of a machine’s attack surface, the power they wield, and their complexity. At the same time, kernels can also be byzantine and idiosyncratic. Before we attempt to create generated parsers for wide deployment into kernels, therefore, it is important to understand how much of the existing codebase those parsers will replace, what (if any) functionality beyond parsing the generated code will need to implement, and what kernel facilities the code must integrate with. To answer these questions, we analyzed three protocol implementations in each of three open-source kernels to understand their behavior and organization. We identified commonalities and differences, measured the quantity of code used for different purposes, identified when and how multiplexing decisions were made, and analyzed the order in which operations were performed. We intend this analysis to inform the creation of generated parsers, for a variety of protocols, that can be smoothly integrated into modern kernels.