Reviewerly/exHarmony huggingface dataset:
Phase Shifting Interferometry Techniques for Surface Parameters Measurement

This chapter discusses the temporal phase shifting interferometryPhase shifting interferometry (PSI) methods developed at the Karpenko Physico-Mechanical Institute of the NAS of Ukraine. The two-stepTwo-step interferometry method with an unknown phase shiftUnknown phase shift of the reference waveReference wave is analyzed. In this method, the extraction of the unknown phase shiftUnknown phase shift is fulfilled using the population Pearson correlation coefficientPopulation Pearson correlation coefficient (PPCC). This method allows extraction of both 3D surfaceSurface nanoreliefNanorelief and 3D surfaceSurface macrorelief, including 3D wavinessWaviness and form. However, the high error level in the retrieved phase mapsPhase map induced the creation of an iterative method of two-stepTwo-step interferometry with the unknown phase shiftUnknown phase shift. To speed up the interferogramsInterferogram recording and remove the reference and object wavesObject wave registration, a method of three-step interferometry with unknown phase shiftsUnknown phase shift is proposed. In order to expand the range of surfaceSurface roughnessRoughness reconstruction, the two-stepTwo-step dual-wavelength interferometryDual-wavelength interferometry method (TS DWIM) was elaborated. In this method, only two pairs of interferogramsInterferogram are generated and each pair is differed only in an arbitrary phase shiftArbitrary phase shift of the reference waveReference wave. Since the method does not require phase shiftPhase shift calibration, it is enough to use only one arbitrary phase shiftArbitrary phase shift to record both pairs of interferogramsInterferogram. The three-frameThree-frame two-wavelength interferometry method with unknown phase shiftsUnknown phase shift between interferogramsInterferogram is similar to the TS DWIM. However, it does not require additional recording of the object and reference wavesReference wave, which significantly increases its performance.
Features Extraction of Reconstruction Model Using in Augmented Reality System of Teleoperation Mobile Robots

In manned deep space exploration, teleoperation robot can cooperate with or even replace astronauts to perform various dangerous tasks in unfamiliar environment. By combining augmented reality system with teleoperation robot, the higher reliability and accuracy required by teleoperation robot can be obtained. The real-time tracking algorithm is one of the key and difficult points in the realization of on orbit teleoperation augmented reality system. Feature extraction based on reconstruction model is the basis of real-time tracking of the system. This paper studies the feature extraction algorithm based on reconstruction model, analyzes the feature points extraction of Harris and sift from the principle level combined with the task characteristics, studies the relationship among the time, image size and number of feature points of the two kinds of feature extraction through experiments, and tests and analyzes the practical application effect of the selected feature extraction methods in the system, and obtains the SIFT The conclusion is that feature point extraction is more suitable for on orbit augmented reality teleoperation system, and the future work is prospected.
Research on the Development of Intelligent Space System (ISS)

First, the research scope of Intelligent Space Systems (ISS) is clarified, and then the domestic and foreign development of intelligent space systems in three directions is investigated, including intelligent information processing, autonomous task planning and intelligent information interconnection. By comparing and investigating, the scientific problems existing in the development of intelligent space system at present are put forward, and the analysis of the development trend of key technologies of intelligent space system in the future is finally formed.
Sparse Bayesian Approach for Learning Control Barrier Functions and Safe Persistent Coverage Control

In this paper, we propose a persistent coverage control method to safely explore unknown environments using an environmental model learned by the sparse Bayesian approach. A sparse Bayesian classification model is introduced to estimate safety from the obtained partial environmental data by LiDAR sensors. Then, based on the control barrier function method, we propose a control law to cover the unknown environment while guaranteeing the safety of robots using a sparse Bayesian classification model. We also propose an algorithm sequentially updating the sparse Bayesian classification model with new datasets obtained through safe coverage control. Finally, we verify the effectiveness of the proposed algorithm through simulations.
New Methods of Speckle Metrology in Analysis of Rough Surfaces

The method of three-frameThree-frame digital speckle pattern interferometryDigital speckle pattern interferometry (DSPI) (TF DSPIDigital speckle pattern interferometry (DSPI)) with unknown phase shiftsUnknown phase shift of a reference beamReference beam is presented in this chapter. In contrast to the two-stepTwo-step digital speckle pattern interferometryDigital speckle pattern interferometry (DSPI) method discussed in Chap. 4 , registration of the reference and object wavesObject wave intensities is removed in this method. In this regard, speckle interferogramsSpeckle interferogram (SI) (SIsSpeckle interferogram (SI)) are recorded using the integrating-bucket technique, which increases the rate of SIsSpeckle interferogram (SI) registration before and after applying loads. In this method, the sample Pearson correlation coefficientSample Pearson correlation coefficient (SPCC) can be used for extraction the unknown phase shiftUnknown phase shift between SIsSpeckle interferogram (SI). The TF DSPIDigital speckle pattern interferometry (DSPI) method was used in fringe projection interferometryFringe projection interferometry for retrieval of the surface reliefSurface relief. During experiment several real surfaceSurface areas were restored, for example, the 3D surfaceSurface shape of the diamond drill, including its form and surfaceSurface roughnessRoughness.
Research on Integrated Operation Design of Low Orbit Remote Sensing Satellite for Intelligent Application

In this paper, from a remote sensing satellites that orbit earth operation characteristics and application conditions, combined with the elliptic orbit agile imaging and application characteristics of huge amounts of data transmission, and both remote sensing satellites that orbit earth in the next 10 to 20 years on the integration of electronic systems and application requirements of the system software, in view of the existing electronic system, the application of gap is given star to integration of heaven and earth satellite data application interface design, This paper provides a direction for the system software design of the future integrated electronic system on board.
Research on Financing Mode of New Agricultural Operators Under the Background of "Blockchain + Big Data"

Due to financing difficulties, NABE are hard to make progress. The main reasons are the information asymmetry between the financing parties, the lack of effective collateral and their own limitations. This paper will innovate the traditional financing mode by using the characteristics of Blockchain that is not easy to tamper with and decentralized, as well as the powerful functions of data mining, analysis and processing of Big Data technology, so as to establish a trust mechanism to solve the financing problems of new agricultural financial entities.
Research on Load Aggregator Trading Strategy Based on Block Chains

Block chains are characterized by distributed, highly redundant storage, time series data, non-tampering and forgery, decentralized credit, automated smart contracts, security and privacy protection. This makes block chains technology not only be successfully applied in the field of digital encrypted currency, but also in a wide range of economic, financial and social systems. Firstly, the transaction strategy of load aggregator in power retail market is introduced. Then, a new load aggregator trading scheme based on block chain technology is presented. Subsequently, simulation tests are carried out to verify that this method makes the transaction of load aggregator more efficient and economical.
Data Cleaning and Data Analysis of Big Mart Data Based on Python

In this paper, I am committed to using machine learning to predict the sales of goods based on the sales data of stores. Among them, I firstly understand the meaning of the data set, trying to dig deeply to found out the hidden relationships that are significant for further exploration. Then, for missing value, I filled them with predicted values. As for the categories that can be optimized, I modified them in order to get more accuracy prediction. After that, I used 2 methods which are linear regression and random forest to predict the method and made a comparison between these two methods. Eventually, I used the better method to predict the coefficients of the data.
Cloud Storage Data Integrity Verification Scheme that Users Can Revoke in Real Time

With the evolution of cloud storage and the limitations of traditional storage methods, more and more enterprises choose to store data on cloud servers to decrease local storage and maintenance overhead. However, when data is stored on the cloud, no copy is stored locally, so how to ensure the integrity of information on the cloud is significant research topic. In order to efficiently resolve the problem a cloud storage data integrity verification scheme that can be revoked in real time by users is proposed. This paper introduce a trusted third-party proxy to the traditional proxy re-signature technology to complete the re-signature of the data in the cloud for the new user, so as to avoid the untrusted cloud server leaking the signature and damaging the data integrity; Distribute an administrator attribute for users, so that users can revoke in real time according to their actual tenure; Finally, a random mask is used in the audit challenge, which effectively prevents curious third-party audits from recovering and revealing the original data by verifying the information. Security analysis and performance analysis demonstrate that the scheme is secure and effective.
Internet Finance Research Based on Blockchain Technology

With the continuous development of block-chain technology and the Internet finance industry, its risks have become more and more obvious, and will become an important factor restricting the development of Internet finance in the near future. Therefore, only by continuously strengthening the awareness of risk evolution and risk prevention, and thus effectively avoiding risks, can this problem be effectively solved. The purpose of this article is to study the risk evolution and prevention of Internet finance under block-chain technology. This article summarizes and summarizes the risk evolution process of Internet finance under block-chain technology, analyzes the actual situation of the current three stages of Internet finance development, and interprets the problems that exist in the current Internet finance development process through case analysis and the cause of formation is analyzed. This article puts forward suggestions on strengthening China's Internet financial risk prevention, in order to better improve and promote the effectiveness of Internet financial risk prevention. According to survey data, in 2015, my country's Internet transaction value reached 600 billion Yuan, and by 2020, the transaction value reached 65260 billion Yuan. It can be seen from this that in recent years, my country's Internet financial transaction volume has always maintained a rapid growth trend.
Image stitching using sigmoid function to create perception mask

The point features of low-texture images are insufficient and unreliable, so it is difficult to achieve good alignment and easy to damage the image structure. To solve these problems, in this paper, we propose a new image stitching method by using the sigmoid function to create perception mask. Firstly, the point features and line features are used to improve the accuracy of image registration and the naturalness of distortion. Secondly, an energy function is used to optimize the alignment model. Finally, we propose to use sigmoid function to create perception mask image to reduce artifacts and retain image structure. The gradient domain fusion algorithm is combined to achieve image fusion. Experimental results are provided to demonstrate that the proposed method is superior to some previous methods in reducing artifacts and maintaining image structure.
Modified Euclidean-Canberra blend distance metric for kNN classifier

In today’s world different data sets are available on which regression or classification algorithms of machine learning are applied. One of the classification algorithms is k-nearest neighbor (kNN) which computes distance amongst various rows in a dataset. The performance of kNN is evaluated based on K-value and distance metric used, where K is the total count of neighboring elements. Many different distance metrics have been used by researchers in literature, one of them is Canberra distance metric. In this paper the performance of kNN based on Canberra distance metric is measured on different datasets, further the proposed Canberra distance metric, namely, Modified Euclidean-Canberra Blend Distance (MECBD) metric has been applied to the kNN algorithm which led to improvement of class prediction efficiency on the same datasets measured in terms of accuracy, precision, recall, F1-score for different values of k. Further, this study depicts that MECBD metric use led to improvement in accuracy value 80.4% to 90.3%, 80.6% to 85.4% and 70.0% to 77.0% for various data sets used. Also, implementation of ROC curves and auc for k= 5 is done to show the improvement is kNN model prediction which showed increase in auc values for different data sets, for instance increase in auc values from 0.873 to 0.958 for Spine (2 Classes) dataset, 0.857 to 0.940, 0.983 to 0.983 (no change), 0.910 to 0.957 for DH, SL and NO class for Spine (3 Classes) data set and 0.651 to 0.742 for Haberman’s data set.
Incorporating keyword extraction and attention for multi-label text classification

As one of the fundamental tasks in natural language processing, Multi-Label Text Classification (MLTC) is used to mark one or more relevant labels for a given text from a large set of labels. Existing MLTC methods have increasingly focused on improving classification effectiveness by fusing the correlations of labels. Still, the research suffers from difficulties in comprehensively extracting text features and distinguishing similar labels. This paper proposed a multi-label text classification model based on keyword extraction and attention mechanism. The model proposed using keywords to represent labels, adopting both self-attention and interactive attention mechanisms (between labels and text) to extract text features and create text vectors. Finally, fusing text vectors as the classifier’s input. Experiments were conducted on two public datasets and a self-built dataset of illegal advertisements. The experimental results showed that the keyword-based label representation approach proposed in this paper can better obtain label semantics, avoid noise and improve the performance of the multi-label text classification.
Enhancement of video condensation with video browsing and retrieval for surveillance videos using heuristic-derived deep learning algorithm

Video condensation or synopsis is an effective solution for problems regarding video storage and video browsing. The proposed model contributed to developing the video condensation framework for efficient video browsing and video retrieval. In the first stage, the videos are gathered from the surveillance videos. Here, the frames are generated, and then the video backgrounds are extracted. The objects from the frames are acquired through the support of Yolov3. Next, the optimal stitching is done based on the time and object activity of video frames using the Improved Blue Monkey Optimization (IBMO) algorithm. Moreover, video condensation is performed to get the compact video for making better browsing and retrieval of video. The video browsing and retrieval are performed under two phases such as training and testing phases and both phases are done by gathering the videos and followed by the feature extraction using VGG16, where the heuristic improvement is made by the same IBMO algorithm. Then, the extracted deep features from video segments are clustered based on Fuzzy C-means (FCM) clustering for combining the extracted features. These features are stored in the feature database in the training phase. Next, in the testing phase, video browsing and retrieval are performed by considering the queries gathered from the standard dataset. The features of query videos are extracted, which are compared based on Multi-Similarity Function (MSF) with the features in the database for retrieving the video segments. Experimental results show that the developed IBMO-VGG-MSF-based video condensation saves computational loads compared to the previous methods without compromising the condensation ratio and visual quality.
Research on blind watermarking algorithm of encrypted holographic 3d geographic information data based on mapping mechanism

In order to improve the accuracy and security of encrypted holographic 3D geographic information data acquisition and improve the actual resolution of geographic information files, a blind watermarking algorithm for encrypted holographic 3D geographic information data based on mapping mechanism is proposed. According to the characteristics of the mapping mechanism, a mapping mechanism structure diagram is constructed; Under the mapping mechanism technology, blind watermark data is preprocessed. Then, a watermark embedding operation is performed to obtain the watermark information image, and then a blind watermark that encrypts the holographic three-dimensional geographic information data is extracted. Finally, using the blind watermark signal as input, the blind watermark information is obtained by using the watermark strength, and the holographic 3D geographic data information is segmented and encrypted to complete blind watermark detection. The blind watermark algorithm for encrypting the holographic 3D geographic information data is studied. The results show that the maximum difference between the correlation coefficient of the algorithm in this paper and the correlation coefficient of the unaffected algorithm is only 0.04, which has better anti attack performance, high security, good terrain information collection ability, high data accuracy, and can achieve curvature repair of information data.
The Same Name Is Not Always the Same: Correlating and Tracing Forgery Methods across Various Deepfake Datasets

Deepfakes are becoming increasingly ubiquitous, particularly in facial manipulation. Numerous researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. However, naming these labels is often arbitrary and inconsistent, leading to the fact that most researchers now choose to use only one of the datasets for research work. However, researchers must use these datasets in practical applications and conduct traceability research. In this study, we employ some models to extract forgery features from various deepfake datasets and utilize the K-means clustering method to identify datasets with similar feature values. We analyze the feature values using the Calinski Harabasz Index method. Our findings reveal that datasets with the same or similar labels in different deepfake datasets exhibit different forgery features. We proposed the KCE system to solve this problem, which combines multiple deepfake datasets according to feature similarity. We analyzed four groups of test datasets and found that the model trained based on KCE combined data faced unknown data types, and Calinski Harabasz scored 42.3% higher than combined by forged names. Furthermore, it is 2.5% higher than the model using all data, although the latter has more training data. It shows that this method improves the generalization ability of the model. This paper introduces a fresh perspective for effectively evaluating and utilizing diverse deepfake datasets and conducting deepfake traceability research.
“Not all motherfuckers are <i>MENA</i>, but most <i>MENA</i> are motherfuckers”

Abstract The number of unaccompanied foreign minors ( Menor Extranjero No Acompañado, MENA ) in Spain has risen, which has led to them being the subject of discriminatory language and negative stereotyping on Twitter. This study analyses the characteristics of hate speech against MENA on the social platform Twitter and seeks to identify its triggers. A quantitative analysis using text mining and text analytics was performed on 10,776 tweets mentioning MENA published from January 2018 to March 2021. The study concluded that hate speech was built around the terms criminal, rape and steal , and was characterized by use of language that stripped these minors of the features usually attributed to children, a process which can be described as adultification . Similarly, the analysis revealed that the main trigger of hate speech was the way in which the media reported violent crimes attributed to MENA , displaying what was perceived as positive bias towards their integration.
Understanding and appraising ‘hate speech’

Abstract Hate speech has become a matter of international concern, permeating institutional and lay discussions alike. Yet, exactly what it means to refer to a linguistic act as ‘hate speech’ remains unclear. This paper examines the lay understanding of hate speech, focusing on (1) the relationship between hate speech and hate, and (2) the relationship between hate speech and offensive speech. As part of the second question, the paper considers how hate speech is defined as a legal matter in the UK Public Order Act 1986. The study adopts a corpus-based discourse analysis approach and examines 255 hate speech-related news articles and the general English Web 2020 corpus. Hate speech is a complex multifaceted phenomenon; while ‘hate’ is one of its core characteristics, it is not sufficient to assess a certain behaviour as hate speech. Threats, denigration of the targets based on a protected characteristic (age, race, religion, sex, sexual orientation, disability), the potential to cause harm and the intent to stir up hatred are also essential in distinguishing hate speech and offense.
Foundation Models for Information Extraction

Abstract In the chapter we consider Information Extraction approaches that automatically identify structured information in text documents and comprise a set of tasks. The Text Classification task assigns a document to one or more pre-defined content categories or classes. This includes many subtasks such as language identification, sentiment analysis, etc. The Word Sense Disambiguation task attaches a predefined meaning to each word in a document. The Named Entity Recognition task identifies named entities in a document. An entity is any object or concept mentioned in the text and a named entity is an entity that is referred to by a proper name. The Relation Extraction task aims to identify the relationship between entities extracted from a text. This covers many subtasks such as coreference resolution, entity linking, and event extraction. Most demanding is the joint extraction of entities and relations from a text. Traditionally, relatively small Pre-trained Language Models have been fine-tuned to these task and yield high performance, while larger Foundation Models achieve high scores with few-shot prompts, but usually have not been benchmarked.