Reviewerly/exHarmony huggingface dataset:
UCS-MedTranGAN: Unsupervised Medical Image Transformation Using CSGAN

Medical Image transformation is an emerging area in computer vision technology with several applications within the medical field. Supervisedlearning requires pixel-wisepairedco-registered image datasets to perform the image transformation. In realistic situations, gathering these types of datasets is a challenging task. And the unsupervisedimage transformation architecture resulted in the blurred transformed images with non-realistic data. A novel unsupervised medical image transformation model named as UCS -MedTranGAN is proposed. The proposed model uses a new non-adversarial cyclic synthesized losses which reduces the content and perceptual loss in the transformed medical images. This work is compared with other unsupervised transformation architecture which shows the implementation of the proposed model based on the two tasks i) the transformation of Positron Emission Tomography image to Computed Tomography image and ii) rectification of MR movement.
Analysis of English Teaching Data on Cloud Platform Based on Data Mining Technology

In vocational education, English education plays an increasingly prominent role and significance. Analyzing the data generated in English education through data mining technology can help teachers carry out targeted teaching better and help students find problems in learning. This paper focuses on the impact of data mining on College English teaching. Combined with the data model of the cloud platform, four types of data are extracted from the platform, namely the initial scores of the students, the test scores of the students after the experiment, the control group and the experimental group before and after the test, the degree of preference for English courses, learning interest, independent learning ability and cooperative learning. The survey data of ability, etc., and the survey data of the experimental group's adaptability to the teaching model of “learning by learning” supported by educational big data. Classroom teaching observation provides a guarantee for the reliability of the experiment. During the observation, the subjects were not informed in advance. Therefore, the subjects behaved naturally, reflecting the real phenomenon of ordinary English listening classes. Under the assumption that the variances are equal, the P value of the mean T test is 0.117 far greater than 0.05, indicating that there is no significant difference in the performance of the two classes. The results show that improving the college public English teaching syllabus and formulating college public English education policies that conform to the trend of the times have very important practical significance.
A Security Architecture for Cloud Data Using Hybrid Security Scheme

Cloud Computing revolutionize the usage of Internet of Things enabled devices integrated via Internet. Providing everything in an outsourced fashion, Cloud also lends infrastructures such as storage. Though cloud makes it easy for us to store and access the data faster and easier, yet there exist various security and privacy risks. Such issues if not handled may become more threatening as it could even disclose the privacy of an individual/ organization. Strengthening the security of data is need of the hour. The work proposes a novel architecture enhancing the security of Cloud data in an IoT integrated environment. In order to enhance the security, systematic use of a modified hybrid mechanism based on DNA code and Elliptic Curve Cryptography along with Third Party Audit is proposed. The performance of the proposed mechanism has been analysed. The results ensures that proposed IoT Cloud architecture performs better while providing strong security which is the major aspect of the work.
Nd-Net: Exploring Non-Defective Features for More Robust Inspection of Power Transmission Line Defect

Defect detection in aerial images is a significant w ork in power transmission line inspection. Thanks to the applic ation of object detection neural network, favorable performance has been achieved in detection of common defects. However, d ue to an feature alignment problem of defective and non-defecti ve data has not been resolved in state-of-the-art research, a big gap, between performance of current models and expected accu racy in applications, still exists. In this paper, a novel network a rchitecture, consisting of a multi-label classification head follow ed by a backbone network, is proposed. The backbone network in the architecture is taking advantage of non-defective fittings features in background. Experimental results show that the pr oposed method achieves more competitively capabilities with high accuracy, compared with previous architectures.
Proposed Secure 3-Use Case Diagram

The global use of UML diagrams serving as a baseline for the development of software leads to the mandatory consideration of non-functional requirements providing a secure and robust application. Secure SRS model with CIA-AAA provides the consideration of non-functional requirements and quality attributes before authorizing the users for accessing use cases. The extensive literature survey for UML Use case diagram is done. The UML use case diagrams do not provide notations, stereotypes, constraint for mis-use case and defense mechanism. This paper describes various notations, the new stereotypes such as Authentication, Buffer Overflow, SQL Injection, Encryption, Session Expiration and Connection Flooding with mathematical modelling for mitigating the attacks on the various transactions or use cases handle by the actors for providing the security.
Text Matching Model that Fuse Position-Encoding with Multiple Attentional Mechanisms

Text matching is a fundamental research topic for upper-level applications such as search engines and question-and-answer systems. How to fully understand the text content and mine the effective information is the key problem to be solved in this topic. In this study, we propose a PA-CNN network model that incorporates text location information and uses multi-angle co-attentive computation. The model generates a corresponding location representation for each word vector by location Embedding, which helps the model to better learn the relative location relationship between features. Then the deep extraction of features is performed using multi-scale CNN with dilated convolution. Then, we use a combination of multiple attention mechanisms to perform alignment operations between feature pairs, and finally, use segmented pooling for key screening retention. After experimental evaluation, our model can achieve over 89% accuracy on the natural language inference task, over 90% accuracy on the question interpretation recognition task, and 75.33% and 77.35% MAP and MRR metrics, respectively, on the answer selection task, all outperforming the existing baseline model.
Iterative Zero-Shot Localization via Semantic-Assisted Location Network

This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin.
Retraction: Smart Home Automation System Using Wireless Sensor Networks (J. Phys.: Conf. Ser. 1916 012221)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022
Weighted video simplification model based on content complexity

The amount of surveillance video data is increasing, which is difficult to store. At the same time, the moving target distribution of surveillance video is sparse and inconsistent, which is not easy to view. Therefore, this paper designs a weighted and graded simplified video model in prior scenes. Through the recognition and analysis of moving objects such as people and objects in the video, the simplified video is designed to make it easier for relevant people to browse its information and reduce the amount of video data. The compression rate and the similarity with the original video were used as parameters to compare the same frame difference method. When the compression rate is similar, the similarity between the proposed method and the original video is higher than that of the frame difference method. The validity of the proposed method is verified.
Improved Real Time Object Detection Method for Remote Sensing Image Based on YOLOv4

High resolution remote sensing image contains rich information. Object detection technology is the core technique of remote sensing image analysis. Aiming at the problems of slow object detection speed and low performance of dense small object detection in remote sensing image, an improved real-time remote sensing image object detection method based on YOLOv4 is proposed. This method designs an image segmentation algorithm for high-resolution remote sensing image, which reduces a mass of information loss in the process of image scale transformation. The feature of remote sensing image is extracted by CSPdarknet53 network and modified by inserting the SE block in attention mechanism to strengthen the effective channels, and the information is effectively utilized by SPP and PANet modules. By modifying the loss function and adding a hyper-parameter to suppress the influence of imbalance between the object and background class on the training process. Experiments verify the effectiveness of ImYOLOv4, showing that our proposed approach can outperform the baselines.
DCV: A Taxonomy on Deep Learning Based Lung Cancer Classification

Deep learning based on lung cancer classification has been used increasingly for the early diagnosis for several reasons such as lack of robust deep learning-based system, complexity of nodule structure, lack of proper lung segmentation technique, high false positive result, lack of best feature extraction and less amount of medical imaging data for training deep learning model, it has been difficult to get high classification performance. The aim of this paper getting high lung cancer classification performance. We introduce the Data, Classification technique and View (DCV) as main components of the system that concern for the better lung cancer classification results, along with them different intermediate components such as Lung nodule segmentation, Feature extraction, Feature reduction are also defined. These components are key for providing better classification performance result which helps radiologist for early diagnosis of lung cancer. We have proposed uses image data having different dimensionality as input to the deep learning based classifier which provides lung cancer classification to be viewed by radiologists for the early diagnosis of lung cancer.We evaluated the proposed DCV system by classifying 30 state-of-art research papers in the field of deep learning based lung cancer classification system. Through this paper, readers will get the result of deep learning based lung cancer classification system. Also, readers will understand the classification groups, validation criteria, future gaps of the 30 literature.
Scaling Wavelet Analysis of Chaotic Systems

In this work, a scaling analysis based on the wavelet discrete transform is carried out to time series that present different chaotic dynamics. We were able to characterize the behavior of the data, which can be considered as a security criterion in different applications such as in an image encryption system.
Private Data Hiding System Using State-Switch DWT Coefficients Quantization on Digital Signal

The watermark embedded by traditional methods is easy to be lost under some attacks. To overcome this problem, this study proposes a novel method based on DWT. It adopts a digital audio watermarking state-switching system which optimizes DWT coefficients doubly. Firstly, it combines the quantization-embedding system and the weights of DWT coefficients with SNR to obtain an optimization model for watermarking. Next, the Lagrange principle, Hessian matrix, and minimum energy play three essential roles to obtain the optimal DWT coefficients and weights. Moreover, the almost invariant feature of the optimal weights holds demonstrating resistance to amplitude scaling. Compared with similar algorithms, the experimental results verify that the embedded audio in the proposed method has higher signal-to-noise ratio (SNR) and lower bit error rate (BER). At the same time, it indicates stronger robustness against various attacks, such as re-sampling, amplitude scaling, and mp3 compression.
An Improved Long-Term Anti-Masking Object Tracking Algorithm

research-article An Improved Long-Term Anti-Masking Object Tracking Algorithm Share on Authors: Jingjing Zhao Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaSearch about this author , Xihai Xie Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaSearch about this author , Yao Tang Xi'an University of Posts and Telecommunications, China Xi'an University of Posts and Telecommunications, ChinaSearch about this author Authors Info & Claims AIPR 2021: 2021 4th International Conference on Artificial Intelligence and Pattern RecognitionSeptember 2021 Pages 14–21https://doi.org/10.1145/3488933.3488963Online:25 February 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development and Realization of Scientific Research Management Information System Based on Process Attempt and Agile Development

This paper studies the development and implementation of scientific research management information systems based on process attempts and agile development, and summarizes the related concepts of traditional proj ect management systems. Secondly, it introduces the characteristics of process attempts and agile development management techniques. The process attempts to analyze with agile development technology and apply this technology to the scientific and technological plan project management system. From system requirements analysis to specific design, as well as the specific realization ideas and flowcharts of key parts of the system, the design and realization process of a scientific research project management system based on process attempts and agile development technology are described, and the efficiency is increased by 4.69%.
Auditory Communication Platform and Higher Vocational Smart Guiding Management Based on Embedded System

Based on the difference between the real-time nature of embedded systems and ordinary operating systems, this paper studies auditory communication as an advanced multi-channel communication method, and adds artificial intelligence algorithms to the embedded system design, which increases the sending and receiving rate of auditory communication by 14%. Discussed the problems existing in the management system and operating mechanism of my country's higher vocational education. The last chapter is the focus of this article. It analyzes the public management countermeasures of improving the management system and operating mechanism of higher vocational education in my country from the aspects of clarifying government functions, improving market competition mechanism, incentive evaluation mechanism, and taking the “customer” orientation.
Self-studying environment with imagery rehearsal for a ball-carrier in basketball

This study focuses on a player who carries a ball in basketball and develops a learning support environment for the player&#x2019;s selecting action. In this study, the targeting actions for next time are passing, dribbling, and shooting as the basic actions for the selection. In order for learners to select an action according to the situation, we apply the Delaunay diagram, which is defined from a two-dimensional overhead view of the court, to automatically extract possible options of the next actions. The system applies an estimation indicator in respect to shooting possibility to the extracted options. The indicator is estimated based on the positions of the ball carrier, other players, and the goal represented in the game data. This indexed value is called "My Recommendation" indicating the degree of potential success. We developed a system to present the extracted action options and the degree of recommendation to a learner. When a 2D-animation is shown in this system, the action options are suggested by colored arrows starting from the ball carrier to the target option. Through the prototype use, learners could learn typical behavior patterns.
CSCP-Net: Cuboid-Shaped Child-Pyramid Augmentation

Feature fusion is an important trick to improve the performance of object detectors at present. When extracting features of a certain scale, we must consider the semantics of all feature points in a large neighborhood centered on this feature. With the expansion of the reception field, the information entropy of feature points decreases and becomes easier to learn. Therefore, we propose a new feature fusion method – Look Around, which is different from the previous FPN, PAFPN, BiFPN, etc. Our feature fusion will make full use of the relationship between these feature points and supplement the semantic information of target feature points by neighbors. After extensive experiments on the PASCAL VOC dataset, the result shows that the Look Around Fusion method improves mAP by 3.5%, which is better than FPN and FSSD.
Online Interactive Data Extraction and Intelligent Analysis of College Vocal Music Courses Oriented to Mobile Android Platform

This paper studies the extraction and intelligent analysis of online interactive data of college vocal music courses oriented to mobile Android platforms. First, it understands the traditional learning mode of college bel canto vocal music teaching courses, uses the Internet of Things sensor network to extract data from college vocal music courses, and then based on Neural network algorithms, many-to-one interactive data extraction for the teaching of sight singing, chorus, and music appreciation, all require teachers to use a variety of methods to teach music in a sound and emotional manner.
The risks brought by blockchain applications to the safe operation of power grids

Abstract The continuous progress of science and technology has promoted the rapid development of information technology and Internet technology. In this context, the blockchain came into being and became an important part of information technology, representing that the information age has entered a new stage of development. This article aims to study the risk analysis of the application of blockchain to the safe operation of the power grid. This paper studies the general situation of the development of blockchain technology and the development of China's power grid. In the method part, it also introduces the improvement of the analytic hierarchy process, historical vulnerability measurement (HVM) and HVM to analyze the security of the power grid. Finally, the risks brought by the application of blockchain to the safe operation of the power grid are introduced, and effective suggestions are given to ensure the safe operation of the power grid. We also conducted a satisfaction survey on the blockchain. From the results of the survey, it can be concluded that everyone's satisfaction with the intelligentization of the blockchain reached 91%.
Research on the Application of Block Chain in Promoting the Consumption of Renewable Energy

The issue of renewable energy power consumption is an important issue that needs to be solved. The combination of block chain technology and energy breaks the traditional concept of energy management and brings new opportunities to promote renewable energy power consumption. This paper briefly introduces the structure of the six levels of block chain and the key technology. And then, the structure of block chain and its decentralization, tamper-proof, traceability, multi-node sharing and other technical characteristics are analyzed. Finally, the application cases of block chain technology in promoting renewable energy power consumption in optimal operation of microgrid, peer-to-peer power trade, green certificate management and electric power transportation are discussed. The result shows that block chain has significant effect on the consumption of renewable energy.
Towards self-stabilizing blockchain, reconstructing totally erased blockchain

Blockchains are commonly used for crypto-currency systems, where they publicly store transactions. In a permissioned blockchain, only a few participants (nodes) can push new transactions to the chain. The known boundary on the number of faulty participants – up to f for 3f+1 participants – may be surpassed, causing the underlying consensus algorithm (BFT) to fail. When a malicious adversary compromises or corrupts enough nodes it may lead to the complete corruption of the ledger and even to the destruction of ledger copies the nodes hold. We suggest a reconstruction solution for the blockchain in the event of such an attack. We present a technique using verifiable secret sharing (VSS), making our solution immediate, requires no trust between the different blockchain parties, and provides each user an independent reconstruction. Our solution enables the exposure of an end-user's private key in case of dishonest reconstruction, thus, removing the incentive of such an act.