Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Multimedia Content Protection

This chapter presents a comprehensive overview of the methods and applications of blockchain technology for multimedia content security. These applications are categorised using a taxonomy that takes into account the technical features of blockchain technology, types of blockchain, content protection strategies including encryption, digital rights management, digital watermarking, and fingerprinting (or transaction tracing), as well as performance standards. Moreover, multimedia-based content protection techniques have been covered in this chapter. According to a review of the literature, there is currently no comprehensive and organised taxonomy specifically devoted to blockchain-based content protection solutions. The comparative study is of the most noticeable work done on blockchain-based content protection techniques, which is highly cited by the authors.
An outlier detection and recovery method based on moving least squares quasi-interpolation scheme and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mtext>ł</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mtext>ł</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math>-minimization problem

In the scattered data used for function approximation, there may be some data that deviate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of outliers to construct a ł0-minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the outliers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the problem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical.
Detection of Composites and Sandwich Structures for Aeronautic Application

"An overview of sandwich structures in aviation applications is provided in this article. It emphasizes the complexity of designing these structures and addresses the main issues that designers face while working with them. Beginning with early instances from the 1930s and focusing on their significant development during World War II, the article examines the evolution of sandwich structures. It explores their wide range of uses in both civil and military spheres. The article also investigates the impact of polymer materials and sheet technology on the mechanical characteristics of composite sandwich constructions. The essay covers three different types of sandwich structures that were created using manual lay-up, press technique, and autoclave application manufacturing processes. These sandwich specimens underwent impact load tests to determine their failure properties. The structural analysis focused on sandwich panels produced in a similar manner, with an adhesive layer between the cores. The goal of the study was to generate research findings about the effects of stress during sandwich panel fabrication on several mechanical properties of structured sandwich composites, including flexural strength, impact strength, and compressive strength.
Comparisons of machine learning techniques for detecting fraudulent criminal identities

This paper focuses on applications of various machine learning techniques on an anonymized policing dataset used in EU SPIRIT Horizon 2020 project to identify fraudulent identities and help Law Enforcement Agencies (LEAs) in their investigation in finding potential criminals and identity resolution. Lack of qualitative data and appropriate methodology to carry out research on criminal fraudulent identities is a common reason for fewer research in this area. Additionally, it is a very sensitive data to work with and minor inaccuracy in prediction of result causes massive impact in the society as genuine people could be questioned whereas criminals could be sent free. Both of these issues are addressed in this paper by application of 39 million records from policing dataset and working towards higher accuracy while building the model. Various machine learning approaches are applied to train the dataset to make predictions and the research focus on being able to predict the 5 suspected fraudulent identities out of 39 million records in the policing dataset. One of the applied machine learning techniques include TensorFlow along with Keras model which has seldomly been applied by researchers in detection of criminal data. To compare the results and test accuracy of TensorFlow model, other machine learning techniques such as Support Vector Machine, Naïve Bayes and K-nearest Neighbours are also applied to have a comparative study on the obtained outcomes from each model. The goal of this research is to find fraudulent IDs amongst all the anonymized IDs in the criminal dataset using TensorFlow and three other machine learning models and select the most optimal model out of them. Since the model is comparing two names so string-matching techniques such as Levenshtein edit distance, Hamming Distance, Jaro-Winkler and Soundex were applied to select an effective approach first before building the model and analysing the results. TensorFlow model demonstrated highest accuracy with relatively least execution time and the only model to successfully predict all the 5 suspects from the policing dataset.
A Method for Determining Partial Discharge Defect Sources in GIS Online Monitoring

This paper designs a method for identifying the local defect homology in ultra-high frequency(UHF) on-line monitoring of gas insulated switchgear(GIS) equipment, and uses the time correlation and data similarity of the measured points to find out the source of local discharge defect in the substation. It further combines the energy characteristics of the local discharge signals at each station with the physical topological relationship of each station to identify whether the local discharge signals are internal signal or external interference. This method solves the problem of how many discharge signals there are and whether these discharge sources are internal or external when a large number of local measurement and point alarms are on site. In addition, this method uses Jaccard distance to measure temporal correlation of time series data based on clustering characteristics. Multidimensional local discharge eigenvalues are constructed from very high frequency phase resolved pulse sequence(PRPS) data, and the data similarity between different measured points is measured based on weighted Euclidean distance. Combines the topological characteristics of the deployed points with the energy decay characteristics of the measured point signals to identify whether the homologous signals are internal or external interference. This method has the features of high recognition accuracy and high practicability.
A Data File Encoding Method Applicable to Condition Monitoring System of Nuclear Power Plant

Nuclear power is a kind of clean and renewable energy and the safe and stable operation of nuclear power reactor and its critical equipment has attracted much attention. Due to the technical iteration and long-term operation of the nuclear power plant condition monitoring system, which is used for early fault warning and providing the operation and maintenance decision scheme, the data files generated by those systems gradually appear problems such as disunity of analysis or decoding rules and reduction of data standardization. This paper proposes a standardized 8-Byte data file encoding method applicable to the condition monitoring system. By specifying the special data file structure and related specifications, it realizes the fast and simple reading and decoding of the data file without any kind of analysis rules, and fully improves the data standardization and utilization efficiency. After practical application, this 8-Byte method proposed by this paper shows better effect than the traditional method, and can be extended to other links of the power system.
A Construction Method for the Knowledge Graph of Power Equipment Based on Ontology Modeling

Knowledge graph technology can make full use of the vast amount of data stored in power grid to describe the core elements of power equipment in a structured form. So as to form a professional knowledge system of power equipment, effectively use the value of it, and promote the intelligent development of power grid. This paper proposes a construction method for the knowledge graph of power equipment based on ontology modeling, which can extract and integrate equipment data from massive historical data accumulated by power grid, and realize the creation of power equipment knowledge graph through knowledge modeling, knowledge extraction, knowledge fusion and other links. Finally, the relevant process and visualized display are demonstrated through an example. The results show that the knowledge graph of power equipment can effectively integrate the information in the power grid, realize the unified description of power equipment data, and provide a basis for the research of intelligent power grid.
Intelligent Monitoring Method of Helmet Wearing Identification in Power Plants

To handle the problems of low recognition accuracy and slow detection speed of the current safety helmet wearing detection in power plant management, an improved safety helmet wearing detection algorithm based on YOLOv5 is proposed. Firstly, based on the idea of the weighted bidirectional feature pyramid network BiFPN, the weighted BiFPN network structure is developed. The FPN + PAN feature fusion structure in the original neck part of YOLOv5 is modified to the weighted BiFPN network structure, which reduces the feature information lost in convolution operation and improves the accuracy of safety helmet detection. Secondly, the CBAM attention mechanism is integrated into the YOLOv5 network to obtain relatively fine safety helmet feature data points. Finally, by using Kmeans + + clustering algorithm, the selected anchor box is closer to the optimal solution. Experiments show that the mAP of the improved algorithm is 90.44 %, which is 6.15 % higher than that of the original algorithm by using the frozen and non-frozen training method under the self-built experimental data in this paper.
Comprehensive performance evaluation method of Owner's Project Manager-Intelligent Management Platforms

The construction of an Owner’s Project Manager(OPM)-Intelligent Management Platform(IMP) will help to comprehensively improve the management level of the owner's project department of the power grid project. This paper comprehensively considers the concerns and demands of all relevant parties, sorts out the comprehensive performance evaluation index system of the OPM-IMP. A comprehensive performance evaluation method of the platform based on the CRITIC fuzzy comprehensive evaluation was proposed to evaluate the comprehensive performance of the OPM-IMP. The example verification shows that: the comprehensive performance evaluation method of the OPM-IMP proposed in this paper can effectively reflect the construction and operation status of the platform. The comprehensive performance evaluation model based on CRITIC fuzzy comprehensive evaluation can scientifically and reasonably evaluate the comprehensive performance of the OPM-IMP.
Cascading Event Analysis in Distribution Networks Based on High-Dimensional Factor Models

Cascading event detection is essential for situational awareness and the secure operation of distribution networks. In this paper, based on high-dimensional factor models (HDFMs), an approach is proposed for the decomposition and spatial localization of cascading events. The HDFM divides the raw online monitoring data into factors (spikes, indicating event signals) and residuals (a bulk, indicating noises or normal fluctuations). The estimated number of factors is employed as the indicator to detect the occurrence of subevents. In addition, the autoregressive rate of residuals measures the changes in the temporal correlation of noises to track the system's operating state. Case studies verify the proposed approach.
In Defense of Facelessness

Abstract Christy Tidwell reflects on the shift from teaching in person to teaching online asynchronous classes during COVID-19. This shift involved a combination of labor-based grading and using Discord as a central space for the class, both of which aimed to center and engage students and relationships with students rather than further automate the class. Tidwell concludes by commenting on ways that these tools and techniques remain useful even when returning to the in-person classroom.
Harmonic State Estimation Based on Network Equivalence and Closed Loop GAN

Harmonic state estimation is the main method of harmonic source location and harmonic responsibility division, also is a necessary means of reduction harmonic hazard. However, when some regions in the power system are unobservable, the traditional harmonic state estimation methods cannot estimate accurately due to the underdetermined state equations. In order to solve the above problems, a harmonic state estimation method based on the network equivalence and deep learning is proposed in this paper. Firstly, network equivalence and coefficient matching are proposed for locating harmonic source. Then, the harmonic state estimation model is constructed based on generative adversarial network (GAN), and the mechanism equation is fused in the objective function to make the model include the power grid information. Finally, the accuracy of the proposed method is verified on IEEE33 system. The results show that the proposed method can accurately locate harmonic source and has high precision of state estimation.
Design of Intelligent Early Warning System for Personnel Strayed in Hazardous Area of High Voltage Test

Aiming at the problems of high labor consumption, poor flexibility and relative lag in the safety protection measures in the dangerous area of high-voltage test, an intelligent early warning system for people break in danger area of high voltage test by mistake based on video surveillance is designed. Firstly, YOLOv4 and DeepSORT are used to track and calculate pedestrian trajectories, predict the travel direction and trend ac-cording to the obtained trajectories, and judge and warn the travel state of people according to the early warning rules. Using different scenarios in the high-voltage test site to conduct simulation tests, the results show that The system can effectively identify the state and trend of the movement of people in the test site and surrounding areas, and has the ability to predict the behavior of stray in; When a hidden danger occurs, the test can be stopped in time to ensure the safety of high-voltage test. The design of this system can provide a reference for the establishment of a safe and reliable, flexible and efficient, intelligent and perfect high-voltage laboratory.
Ensemble Learning Solution for the Aspect-based Sentimental Analysis on IMDB reviews

Nowadays, social media significantly influences how people form opinions about any type of business, politics, commerce, etc. based on user ratings. These reviews were examined using the field of sentiment analysis. This is a crucial component since well-designed and carried out sentiment assessments may lead to better and more accurate estimates in both business and politics. Sentiment analysis is skilled at overcoming a variety of difficulties, including issues with accuracy, problems with binary classification, problems with polarity change and data scarcity. There have been several approaches proposed and developed for this, but none of them have been effective in consistently extracting sentiment analysis. We reviewed the traditional lexicon-based method and then we developed an ensemble model employing machine learning algorithms that outperformed the lexicon-based approach by 89 percent. Additionally, we have shown through a comparison study why the suggested model is the most effective. The stacking classifier ensemble strategy that we utilized in this case allowed us to boost classification accuracy by 1% while utilizing a variety of well-known machine learning algorithms.
Disaster-news datasets for multi-label document classification, sentence classification, and abstractive document summarization tasks

Mining of disaster-news articles can deliver highly useful information for the authorities in critical decision making and also for awareness dissemination among people, at disaster situations. A set of disaster-news articles containing more than seven thousand and six hundred news documents on COVID-19, storm, flood, heavy rain, cloudburst, landslide, earthquake, and tsunami is put forward for interested researchers to explore. It includes more than three thousand news articles only on COVID-19 considering it a disaster event. It also includes more than 4.5 thousand articles on natural disaster events prevalent in India. A dataset has been prepared for the sentence classification task from the COVID-19 articles. An abstractive summarization dataset has also been prepared for the task of automatic generation of a suitable headline for a disaster-news article. It is done by considering the articles and their titles as text and summaries. Another dataset has been prepared by combining COVID-19 and the natural disaster articles for three tasks; first, identification of the events in an article, second and third, identification of the sentences containing disaster-location and disaster-impact information respectively. The Precision, Recall, F-measure, and Accuracy scores after applying the Random Forest classifier on both datasets are presented in this paper that show impressive results.
Understanding DeepFool Adversarial Attack and Defense with Skater Interpretations

With the incorporation of artificial intelligence in businesses, particularly features like computer vision, it has become increasingly important to ensure the robustness of the models being used. A popular technique used to exploit machine learning models is an adversarial attack. Adversarial attacks mis-lead a predictive model by providing it with perturbed input. In the context of computer vision, it involves creating perturbations in an image to deceive a model. One such adversarial attack is the DeepFool attack, which aims to create the most minimal perturbations to an image to deceive the model. These attacks can also affect the way in which interpretations are made. In this paper, we analyze the DeepFool attack and its countermeasures on the ResNet-50 model running on the NIH malarial dataset. To assess the efficiency of the attack and subsequent adversarial training, we have used accuracy and loss. The nature and impact of the attack and adversarial training are analysed using skater, a model interpretation framework. The variations in the interpretations when adversarial attacks are in place are also analysed.
Efficient FPGA Implementation of RSA Algorithm Using Vedic Multiplier

Technology cannot function effectively and securely without algorithms, which also enable integrity and encryption. To secure sensitive data, especially when it is delivered over an unsecured network like the Internet, we employ the RSA (Rivest-Shamir-Adleman) Algorithm, which forms the backbone of the cryptosystem that permits public key encryption. In a cryptosystem, multipliers are essential since they help to produce the desired results as efficiently as possible. The enormous number of adders and other digital circuits used in typical multipliers causes an increase in propagation delay, which eventually reduces the multiplier's efficiency. In contrast, the Vedic Multiplier can overcome this issue and operates at high efficiency. The objective is to develop an effective 8 X 8 Vedic multiplier and implement it in the Field Programmable Gate Array (FPGA) using the simulation tool Xilinx - ISE Design Suite 14.7. The effective performance metrics are compared with the pre-existing booth multiplier in terms of combinational path delay, number of slices, and number of Look Up Table (LUT)s. Further, the Modular exponentiation operation in RSA cryptosystem is replaced with the proposed Vedic multiplier and booth multiplier logics. The effectiveness of the RSA implementation with these operator logics is compared in terms of delay and area.
Advantages and Limitations of Quantum Radar

This paper presents the advantages and limitations of a quantum radar system. The use of entangled photons to interrogate a region of space where a target is supposed to be, gives a lower probability of error than using a classical signal. This is possible because of the extra information that the quantum entanglement property can give us. This information helps us to distinguish between the signal reflected from the target and the background noise. Finally, through simulations, it is shown that the actual advantage of the quantum radar is the ability to detect a target in a high-losses and high-noise environment using a weak signal.
Fading Channel Classification Based on Convolutional Neural Network with General Linear Chirplet Transform

This paper proposes the application of Deep Learning and Convolutional Neural Networks (CNN), in particular to the problem of channel classification. The classification of the fading channels is an important function in wireless communications as it contributes to the spectrum awareness of a wireless communication system. This paper proposes an approach based on the transformation of the RF one dimensional signal (e.g., the amplitude component of the signal) to an image using the General Linear Chirplet Transform (GLCT). The results show that the combination of GLCT with CNN is able to outperform the direct application of the CNN, not only on the original one dimensional signal but also on the combination of the spectrogram and CNN as proposed in literature. The approach is applied to a data set of RF weather radar signals generated by the authors, where different fading conditions are created.
Relationship between Cloud Cover and Rainfall Rate in Madrid: First Results

A first analysis of the relationship between cloud cover and rainfall rate has been performed using two years of total cloud cover data provided by ERA5 and experimental rainfall rate measurements collected with a disdrometer at UPM. The results obtained include cumulative distributions and histograms of both parameters, and the scatter plots of total cloud cover against the hourly average of rainfall rate and hourly maximum rainfall rate. The duration of events has also been investigated for both parameters.