Reviewerly/exHarmony huggingface dataset:
Machine Learning Approaches in Developing Expert System for Medical Image Analysis with respect to THR: From Detection to Diagnosis

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment.There is a need to find any deviation that can be acquired in position of artificial femur after the log time of surgery, well in advance thereby overcome the adverse socio economic and psychological burden to both the patient as well as the surgeon.The aim of the study is to develop a noninvasive, ultrasound-based, method of diagnosing acetabular cup loosening at an early stage before any major bone erosion has taken place.The proposed study will build on a previously successful technique for the diagnosis of loosing of the femoral stem component of a THR.This paper highlights the steps like box filter section, mainly first order and second order, followed by key points selection, key points extraction, key point matching and finally finding the deviation through motion vector analysis.The data for this research has been collected from different hospitals in Andhra Pradesh and Tamil Nadu.
Generalized Wireless Adversarial Deep Learning

Deep learning techniques can classify spectrum phenomena ( e.g. , waveform modulation) with accuracy levels that were once thought impossible. Although we have recently seen many advances in this field, extensive work in computer vision has demonstrated that adversarial machine learning (AML) can seriously decrease the accuracy of a classifier . This is done by designing inputs that are close to a legitimate one but interpreted by the classifier as being of a completely different class. On the other hand, it is unclear if , when , and how AML is concretely possible in practical wireless scenarios, where (i) the highly time-varying nature of the channel could compromise adversarial attempts; and (ii) the received waveforms still need to be decodable and thus cannot be extensively modified. This paper advances the state of the art by proposing the first comprehensive analysis and experimental evaluation of adversarial learning attacks to wireless deep learning systems. We postulate a series of adversarial attacks, and formulate a Generalized Wireless Adversarial Machine Learning Problem (GWAP) where we analyze the combined effect of the wireless channel and the adversarial waveform on the efficacy of the attacks. We propose a new neural network architecture called FIRNet , which can be trained to “hack” a classifier based only on its output. We extensively evaluate the performance on (i) a 1000-device radio fingerprinting dataset, and (ii) a 24-class modulation dataset. Results obtained with several channel conditions show that our algorithms can decrease the classifier accuracy up to 3x. We also experimentally evaluate FIRNet on a radio testbed , and show that our data-driven blackbox approach can confuse the classifier up to 97% while keeping the waveform distortion to a minimum.
Time Series Analysis of Cryptocurrency: Factors and Its Prospective

In the recent time as well as the last decade cryptocurrency has been one of the most discussed topic among the researchers all around the world. Different economies across the globe have seen a lot of growth of cryptocurrency over the time and bitcoin especially has seen a growth of 1100% that is why time series analysis of cryptocurrency is of immense significance. Time series analysis can be referred to as the process of taking into consideration a sequence of different points which are observed over a specific time interval. A large number of people start investing into cryptocurrencies without having any knowledge or analyzing the cryptocurrency market because of the hype it has these days and suffer huge losses so designing a model which can predict accurately as to how different cryptocurrencies would behave on basis of previous record can be very helpful and it can help some people in making profit rather than suffering loss. This paper presents a comparative overview of different algorithms like RNN, Linear Regression, GARCH, and ARIMA which can be used for time series analysis and concludes as to which algorithm is best suitable for time series analysis by considering different parameters like RMSE, MAE, etc., Besides this, it also analyzes the different factors which affect the prices of cryptocurrency.
A Covert Communication Method Based on Signal Mimicry Image Generation Technology

Current covert communication methods have poor crypticity and reliability. Inspired by recent successes of Image Processing, we propose a covert communication method based on signal mimic image generation technology. First, the features of digital signals are extracted to generate a 1D array, and the array is mapped to a 2D matrix via a conversion protocol to generate a grayscale image for transmission. Then, an information-hiding algorithm based on the DCT watermark is employed to embed the grayscale image into the carrier image as a watermark. Finally, the image after information hiding is converted into a 1D signal for transmission via the conversion protocol, thus achieving covert communication. Simulation experiments reveal that the proposed covert communication method is feasible and there is no signal loss during processing, indicating good invisibility and interference resistance. Meanwhile, the proposed method overcomes the 1D bottleneck of covert communication and lays the foundation for the cross-application of new technologies.
A Dual Path Saliency Detection with MSER and Grayscale Scarcity

In order to solve the problem of imprecise target detection caused by complex background and similar and small target size and interference size in the saliency detection process of TV camera on targets in field scenes, this paper provides a saliency detection algorithm combining MSER and grayscale scarcity. The results show that this method has strong anti-interference performance, and can eliminate the small clutter background interference and the gradual background interference of the target. The proposed method also overcomes the problem that existing saliency detection methods are difficult to detect small targets. Compared with other classical saliency detection algorithms, the proposed method has the best saliency detection effect.
Research on the Filtering and Tracking Techniques for LSS-Target

The $$\alpha \beta$$ filter algorithm is suitable for tracking uniform movements, it is no longer effective if targets have maneuvered. LSS-Targets are characterized by maneuverability and hard to be tracked by radar, so the radar should give priority to the maneuvering behaviors of targets before filtering. The traditional filtering process of $$\alpha \beta$$ filter algorithm generally has only one association gate which is centered in the predicted position. The new method presented in this paper uses two association gates. The main gate is typical and the auxiliary gate is centered in the previous position. The auxiliary gate is very helpful when the radar tracks LSS-Targets. The performance of this new method is analyzed, experimental results suggest that this new method is competent for tracking maneuvering LSS-Targets.
Continual Learning for SAR Object Detection

Synthetic Aperture Radar (SAR) systems work in an open and dynamic environment, where new classes of interesting objects may be encountered with the changes of time and space. In this continual learning scenario, the existing SAR object detection methods degrade the performance of old classes significantly. In this paper, we propose a continual object detection method for SAR images, which aims to continually detect and recognize new objects from new classes without catastrophic forgetting of previously learned objects. Specifically, to address the class imbalance between old and new classes in continual learning process, we propose a reweighting class-imbalance loss, which can alleviate forgetting. In addition, knowledge distillation on backbone, classification and regression heads is utilized to preserve the knowledge of previous tasks, which can further mitigate forgetting. Experimental results on real SAR object detection datasets demonstrate the effectiveness of the proposed method.
Image Tampering Localization Based on Two-Stream Weighted Fusion Features

When people tried to use existing image manipulation detection methods in real senses, some extreme limitations like lack of robustness against image recompression and resampling were found. In contrast, with the advancement high-quality images become too large to be loaded by neural network. Accordingly, the capability of learning manipulations-sensitive generalizable or even residual semantic features of compressed images becomes a key precondition for the practical value of algorithm. In this paper, we propose a convolutional neural network which uses error level analysis (ELA) to address the interest area and mask semantic information under different image compressions scenarios. We use that network to collect residual semantic features and use a discrete cosine transform (DCT) network to collect compression artifacts. A network which is termed MSEA-Net is realized to explore manipulation detection of recompressed images. The excellent results on NIST16, Columbia, CASIAv2 datasets and post-processed datasets demonstrate the capability of MSEA-Net for recompression and resampling manipulation detection.
A Cybersecurity Protection Framework of the Endpoint System in C4ISR

To address the cybersecurity protection of the endpoint weapon system in C4ISR, the system security is analyzed from data transmission security, software and hardware security and active-passive cybersecurity. A protection framework is proposed to provide targeted cybersecurity protection. For data transmission security, the use of multi-protocol collaboration and lightweight encryption is proposed to ensure the effectiveness and confidentiality of data transmission; for software and hardware security, security measures combining operating system protection, application software protection and hardware protection are proposed; for active and passive cybersecurity protection, attack detection schemes combining dedicated intrusion detection system and dedicated honeypot system are proposed. Finally, several simulation experiments are designed to comprehensively verify the three types of proposed protection schemes. The experimental results denote that the proposed schemes can improve data security without significantly increasing the latency, and can effectively detect network attacks with a detection rate of up to 85% for network attacks.
A Joint Detection and Recognition Algorithm of Non-cooperative Communication Signal Based on Improved YOLOv3

In the modern digital battlefield, the detection and recognition of signals is always the main problem of non-cooperative communication reconnaissance. The rapid development of wireless communication technology brings more challenges to this problem. In this paper, a communication signal detection and recognition method based on improved YOLOv3 is proposed. The method transforms the problem of communication signal detection and recognition to image target detection, which has been perfectly solved by current deep learning algorithms like YOLOv3. Based on this, the paper randomly generates signal data sets containing various modulation types through simulation, and converts the signals to pictures by time-frequency transformation method, and finally achieves the purpose of signal detection and recognition by improving YOLOv3 algorithm. Simulation results show that when the signal-to-noise ratio (SNR) is greater than −5 dB, the joint detection and recognition accuracy of the signal can reach more than 90%.
Research on the Method of ARUAV Task Assignment Based on the Improved PSO Algorithm

In order to facilitate ARUAV (Anti-Radiation Unmanned Aerial Vehicle) coordinated task assignment, this paper proposes a solution based on the improved PSO (Particle Swarm Optimization) algorithm according to the characteristics of the ARUAV employment. The method, in combination with the global search ability of PSO and the local search ability of taboo search optimization, increases the convergence rate and solution accuracy of the algorithm effectively. The simulation results demonstrate the efficiency and feasibility of the method, by which the coordinated task assignment can improve the ARUAV operational effectiveness.
A Survey on Intelligent Writing Technology

With the continuous development of artificial intelligence technology and the boom of Internet content applications, intelligent writing technology for understanding, editing and generating cyberspace information content has become the core technology for cyberspace governance. In this paper, we take a deep review of intelligent writing technology. According to the types of generated content, this technology is divided into three types, namely automatic question answering, automatic text generation, and multimedia generation. We expound the research status of the above three intelligent writing technologies, mainly including intelligent writing products of key enterprises and universities. And make a horizontal and vertical comparative analysis of intelligent writing products. Finally, we summarize the problems of intelligent writing technology at the current stage, which are mainly reflected in four aspects: information security, knowledge dispersion, content quality and copyright protection mechanism.
ADA: An Acronym-Based Data Augmentation Method for Low-Resource Text Classification

Text classification is an essential part of natural language processing. With the development of deep learning technology, using deep learning for text classification has become mainstream. However, deep learning methods often require a considerable quantity of data, and collecting and processing data sets is cumbersome and expensive. This paper presents a data enhancement method simulating the generation of acronyms, quickly expanding the text classification datasets. We show the performance of our method on three classical data sets and compare it with two classical text data enhancement methods. The results show that this method can effectively improve the effect of text classification. Meanwhile, by comparing the similarity with the original sentence, it is found that our method has very little change in sentence semantics and is more robust than the baseline method.
Blockchain-Based Intelligent Data Link Information Architecture Design for Unmanned Cluster

Unmanned Cluster has strong environmental adaptability, rapid penetration ability. Existing single unmanned platform has the problem of low tolerance rate, low effectiveness, difference in mission suitability, weakness of all-stations state sensing ability and to address these problems, more machine-cooperative approaches have been proposed. In view of the requirements of future cross-domain joint operations and intelligent platform operation information sharing of unmanned data chain systems, combined with the block consensus mechanism, on the basis of the consensus battlefield, a number of blockchain technology-based distributed collaborative action consensus are designed. Human platform formation mechanism and process. At the same time, this paper designs a lightweight blockchain structure, proposes a blockchain-based unmanned intelligent cluster data chain information architecture, and builds an information sharing platform suitable for unmanned clusters. This architecture supports operations such as cluster collaborative detection, command and control. Bring a new breakthrough direction for the next generation data link system.
Environment Invariant Feature Extraction Algorithm Based on Radio Frequency I/Q Data Distribution

With the development of the Internet of Things, more and more devices need to be connected to the network. Wireless networks are open, and the network security of devices faces major challenges. The problem of device identification is particularly important. Some traditional signal source marking methods have greatly reduced reliability. The traditional authentication methods mainly rely on software addresses. The radio frequency(RF) fingerprint technology is an emerging device authentication technology, which identifies the device based on the inherent hardware defects of the device itself. The RF fingerprints reflect the inherent characteristics of the device and are difficult to tamper with. In this paper, we proposed an environment invariant feature extraction algorithm based on the cumulative distance. The experiments show that the algorithm proposed in this paper has good robustness. Within the allowable error range, the extracted RF fingerprint features of the same signal in different frequencies, different receivers, and different environments are invariant.
Named Entity Recognition with CRF Based on ALBERT: A Natural Language Processing Model

Named entity recognition has a variety of applications in journalism, where it may extract relevant information from voluminous daily news reports. However, its applicability is limited since there is no word vector learning and existing models are complicated. This paper is based on the lightweight Natural Language Processing model (ALBERT) dynamic word vector generation model proposed by Google. The model was combined with Bidirectional Long Short-Term Memory Network (LSTM) and Conditional Random Field (CRF) to form the ALBERT-BiLSTM-CRF model. This paper applies the ALBERT-BiLSTM-CRF model with the 2014 edition of the People’s Daily published on the Internet as the primary data set to compare the traditional statistical model and the classic NLP model. The experimental results show that the ALBERT-BiLSTM-CRF has a comparative advantage over the classic natural language processing (NLP) model. The proposed model can increase the recognition accuracy and recall rate of named entities in the news. The model's accuracy and recall on the test dataset attained 94.49 and 89.50 percent, respectively, while the model's volume allows for lightweight deployment.
Algorithm for making recommendations in the electronic educational environment based on Markov stochastic models

This article proposes an algorithm for making recommendations for educational resources in the electronic educational environment. The new approach uses the Markov model for evaluating the content of the systems by ordinary users to form the parameters of the initial state, which characterizes the system new user in the form of assessing the first liked resources (system content) to recommend the system interesting elements to an active user. Thus, the problem of “a cold start” is solved for a new user at the first stages of interaction with the system. This problem is inherent to the system under development, since the e-learning system provides a module for making recommendations, which allows it to be classified as a recommendation-based automated system. The new approach proposes combining the use of the Markov process and the time factor to apply them as a single data source for making recommendations. This approach will be based on the principle of analyzing the access of similar users of the system (the similarity is determined by comparing their profiles) at the same time periods. Ease of use is also an integral part of the system being created. Therefore, at the design stage, it is necessary to think over the ergonomics of the recommendations in the educational system.
Learning mixtures of permutations: Groups of pairwise comparisons and combinatorial method of moments

In applications such as rank aggregation, mixture models for permutations are frequently used when the population exhibits heterogeneity. In this work, we study the widely used Mallows mixture model. In the high-dimensional setting, we propose a polynomial-time algorithm that learns a Mallows mixture of permutations on n elements with the optimal sample complexity that is proportional to logn, improving upon previous results that scale polynomially with n. In the high-noise regime, we characterize the optimal dependency of the sample complexity on the noise parameter. Both objectives are accomplished by first studying demixing permutations under a noiseless query model using groups of pairwise comparisons, which can be viewed as moments of the mixing distribution, and then extending these results to the noisy Mallows model by simulating the noiseless oracle.
An outward search to increase population diversity for optimization algorithms

Outward search (OS) is a new scheme proposed to provide diverse forms for improving convergence in evolutionary algorithms. Rather than using new functionalities, OS is performed using the differential-vector equations of an evolutionary algorithm. Three OS schemes are recommended in this study to obtain solutions that improve the performance of an evolutionary algorithm. The first one uses the original equations of the algorithm to generate either an OS solution or a candidate solution. The second utilizes the original equations to produce an OS solution and a candidate solution simultaneously for one individual. The last employs equations of another algorithm to create an OS solution for the studied algorithm. Three bio-inspired algorithms were examined using the CEC2015 benchmark suite to compare the respective performances of the proposed OS schemes. The results of the comparison indicate that searching regions outward from the current area outperformed examining oppositional locations obtained by opposition-based learning.
Development of Music Online Teaching System Based on Distributed Database Search Technology

With the continuous advancement of Internet technology, the traditional music teaching model has slowly evolved from offline to online. Nowadays, music platforms no longer focus solely on music but are supplemented by some social functions. Under the current huge market demand, in order to meet the individual needs of different music users, more and more music platforms continue to perform technological updates. Various music platforms have sprung up continuously. Although they provide users with a variety of options, at the same time, users must download corresponding applications to experience each music platform, which brings a certain degree of confusion to users. In order to solve such problems, this article implements a download-free music online education system through PWA. Aiming at the shortcomings of traditional database search engine systems, this article studies the implementation methods of distributed database search engines. First, a search engine architecture based on a cloud database is proposed so that the existing database search engine system can be implemented in the HBase distributed database, thereby improving the efficiency of the search engine. Secondly, a new data preprocessing method is proposed, which improves the data interaction technology between the relational database and the HBase distributed database, and simplifies the project development framework. Subsequently, this paper proposes a multiuser detection algorithm, which can determine duplicate data entries by calculating keyword similarity to improve the accuracy of search results. This paper promotes the development of music teaching by integrating a multiuser detection algorithm and distributed database search technology and applying it to the construction of music teaching online system.