Reviewerly/exHarmony huggingface dataset:
Fair and smart spectrum allocation scheme for IIoT based on blockchain

Compared to 5G mobile network with a peak rate of 10Gbps, 6G can provide over 100Gbps peak rate, which can better support Industrial Internet of Things (IIoT) popularity and application. Spectrum dynamic access is one of the basic techniques of 6G, and spectrum auction has been gradually applied to mobile communication to solve the problem of secondary users access. To cope with the risk of sudden demand increase of primary users after auction, several channel recall based schemes were proposed. However, the spectrum auction fairness between sellers and buyers as well as fairness among buyers during auction and channel recall process have not been considered in the existing spectrum auction algorithms. Utilizing the unique features of blockchain , a spectrum auction method based on blockchain for 6G mobile network is proposed. The fairness between sellers and buyers and fairness among buyers are guaranteed by smart contracts. The sudden increase of primary users’ traffic demand risk can also be well solved. In addition, in the proposed method, user privacy preservation is provided by blockchain. We deploy the algorithm on Ethereum test network, experimental results indicate that the proposed method is effective in fairness, with a high efficiency and preservation of user privacy.
Blind image deconvolution via salient edge selection and mean curvature regularization

Blind image deconvolution is an ill-posed problem since there exists infinite pairs of blur kernels and latent images. To obtain reasonable results of this problem, most previous methods have emphasized the importance of selecting salient edges for blur kernel estimation. In this paper, a blind deconvolution method based on explicit and implicit salient edges selection is proposed. Explicit edge selection is achieved by using mutually guided image filtering, while mean curvature regularization is adopted to remove disadvantageous structures implicitly. Besides, the proposed model can be efficiently optimized by using half-quadratic penalty method and mean curvature filter. Extensive experiments show that the proposed method performs favorably against the state-of-the-art blind deconvolution methods on both synthetic and real-world blurred images.
Depth Estimation from Light Field Geometry Using Convolutional Neural Networks

Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture containing geometric information. The slope of the line is proportional to the depth of the corresponding object. Considering the light field depth estimation as a spatial density prediction task, we design a convolutional neural network (ESTNet) to estimate the accurate depth quickly. Inspired by the strong image feature extraction ability of convolutional neural networks, especially for texture images, we propose to generate EPI synthetic images from light field data as the input of ESTNet to improve the effect of feature extraction and depth estimation. The architecture of ESTNet is characterized by three input streams, encoding-decoding structure, and skipconnections. The three input streams receive horizontal EPI synthetic image (EPIh), vertical EPI synthetic image (EPIv), and central view image (CV), respectively. EPIh and EPIv contain rich texture and depth cues, while CV provides pixel position association information. ESTNet consists of two stages: encoding and decoding. The encoding stage includes several convolution modules, and correspondingly, the decoding stage embodies some transposed convolution modules. In addition to the forward propagation of the network ESTNet, some skip-connections are added between the convolution module and the corresponding transposed convolution module to fuse the shallow local and deep semantic features. ESTNet is trained on one part of a synthetic light-field dataset and then tested on another part of the synthetic light-field dataset and real light-field dataset. Ablation experiments show that our ESTNet structure is reasonable. Experiments on the synthetic light-field dataset and real light-field dataset show that our ESTNet can balance the accuracy of depth estimation and computational time.
Probit Regressive Tversky Indexed Rocchio Convolutive Deep Neural Learning for Legal Document Data Analytics

Legal documents data analytics is a very significant process in the field of computational law.Semantically analyzing the documents is more challenging since it's often more complicated than open domain documents.Efficient document analysis is crucial to current legal applications, such as case-based reasoning, legal citations, and so on.Due to the extensive growth of documents of data, several statistical machine-learning methods have been developed for Legal documents data analytics.However, documents are large and highly complex, so the traditional machine learning-based classification models are inefficient for accurate data analytics with minimum time.In order to improve the accurate legal documents data analytics with minimum time, an efficient technique called Probit Regressive Tversky Indexed Rocchio Convolutive Deep Neural Learning (PRTIRCDNL) is introduced.The PRTIRCDNL technique uses the Convolutive Deep neural learning concept to learn the given input with help of many layers and provides accurate classification results.Convolutive Deep Neural Learning uses two different processing steps such as keyword extraction and classification in the different layers such as input, two hidden layers and output layer.Initially, large numbers of legal documents are collected from the dataset.Then the collected legal documents are sent to the input layer of the convolutive deep neural learning.The input legal documents are transferred into the first hidden layer where the keyword extraction process is carried out by applying the Target projective probit Regression.Then the regression function extracts the keywords based on frequent occurrence score.Then the extracted keywords are transferred into the second hidden layer where the document classification is performed using the Tversky similarity indexive Rocchio classifier.Likewise, all the legal documents are classified into different classes.The experimental evaluation is carried out using different performance metrics such as accuracy, precision, recall, F-measure and computational time with respect to the number of legal documents collected from the dataset.The observed results confirmed that the presented PRTIRCDNL technique provides the better performance in terms of achieving higher accuracy, precision, recall and F-measure with minimum computation time.
Looking for the link between the causes of the COVID-19 disease using the multi-model application

Based on various reports of COVID-19 caused by a coronavirus, this disease causes numerous problems. There are many factors that can contribute to the rapid spread of COVID-19. In this study, multi-model application is used to identify the association among various variables that cause COVID-19. The use of programmes, artificial intelligence and input data that will be processed as a symptom of the COVID-19 disease, whose data processing uses a multi-model that will classify the variables causing COVID-19. The output data will be presented in a tabular form, so that the readers can easily understand the results. The best result obtained by this programme has an accuracy of 0.979767 using a decision tree with 0.3 of the total dataset as the test data. It is expected that the rate of spread of COVID-19 can be suppressed by detecting the disease using artificial intelligence. In the future, it is expected that a multidisciplinary collaboration between medical science and mathematics can be established with the help of this programme. This enables the authenticity of the research, and the model’s performance be tested based on appropriate data in the field.
Spectral Graph Wavelet Based Image Steganography Using SVD and Arnold Transform

Steganography is the process of concealing sensitive information within cover medium. This study offers an efficient and safe innovative image steganography approach based on graph signal processing (GSP). To scramble the secret image, Arnold cat map transform is used, then Spectral graph wavelet is used to change the cover and scrambled secret image, followed by singular vector decomposition (SVD) of the modified cover image. To create the stego image, an alpha blending process is used. To produce the stego image, GSP-based synthesis is used. By maintaining the inter-pixel correlation, GSP improves the visual quality of the produced stego image. The effects of image processing attacks on the suggested approach are examined. The investigational results and assessment indicate that the proposed steganography scheme is more efficient and robust in terms of quality measures. The quality of stego image is evaluated in respect of PSNR, NCC, SC and AD performance metrics.
Parameterized Extraction of Tiles in Multilevel Gigapixel Images

In many image domains using multilevel gigapixel images, each image level may reveal different information. E.g., a histological image will reveal specific diagnostic information at different resolutions. By incorporating all levels in deep learning models, the accuracy can be improved. It is necessary to extract tiles from the image since it is intractable to process an entire gigapixel image at full resolution at once. Therefore, a sound method for finding and extracting tiles from multiple levels is essential both during training and prediction. In this paper, we have presented a method to parameterize and automate the task of extracting tiles from different scales with a region of interest (ROI) defined at one of the scales. The proposed method makes it easy to extract different datasets from the same group of gigapixel images with different choices of parameters, and it is reproducible and easy to describe by reporting the parameters. The method is suitable for many image domains and is demonstrated here with different parameter settings using histological images from urinary bladder cancer. An efficient implementation of the method is openly provided via Github.
A new bayesian Poisson denoising algorithm based on nonlocal means and stochastic distances

Poisson noise is the main cause of degradation of many imaging modalities. However, many of the proposed methods for reducing noise in images lack a formal approach. Our work develops a new, general, formal and computationally efficient bayesian Poisson denoising algorithm, based on the Nonlocal Means framework and replacing the euclidean distance by stochastic distances, which are more appropriate for the denoising problem. It takes advantage of the conjugacy of Poisson and gamma distributions to obtain its computational efficiency. When dealing with low dose CT images, the algorithm operates on the sinogram, modeling the rates of the Poisson noise by the Gamma distribution. Based on the Bayesian formulation and the conjugacy property, the likelihood follows the Poisson distribution, while the a posteriori distribution is also described by the Gamma distribution. The derived algorithm is applied to simulated and real low-dose CT images and compared to several algorithms proposed in the literature, with competitive results.
Demystifying Artificial Intelligence for End-Users: Findings from a Participatory Machine Learning Show

Interactive and collaborative approaches have been successfully used in educational scenarios. For machine learning and AI, however, such approaches typically require a fair amount of technical expertise. In order to reach everyday users of AI technologies, we propose and evaluate a new interactive approach to help end-users gain a better understanding of AI: A participatory machine learning show. During the show, participants were able to collectively gather corpus data for a neural network for keyword recognition, and interactively train and test its accuracy. Furthermore, the network's decisions were explained by using both an established XAI framework (LIME) and a virtual agent. In cooperation with a museum, we ran several prototype shows and interviewed participating and non-participating visitors to gain insights about their attitude towards (X)AI. We could deduce that the virtual agent and the inclusion of XAI visualisations in our edutainment show were generally rated positively by participants, even though the frameworks we used were originally designed for experts. When comparing both groups, we found that participants felt significantly more competent and positive towards technology compared to non-participating visitors. Our findings suggests that the consideration of specific user needs, personal background, and mental models about (X)AI systems should be included in the XAI design for end-users.
A Survey on Object Detection Methods in Deep Learning

Object detection is used almost in all places nowadays. Locating or identifying objects in a video or an image is possible because of computer vision. It is used in picture retrieval, security, observation, computerized vehicle systems machine investigation, etc. There are several techniques which are developed to detect objects. Some of the methods which are used for object detection are also used for face detection. A survey is done on the most popular methods which are used to accomplish these tasks. These methods are CNN, YOLO, SSD, MobileNet, MTCNN, ResNet, RCNN, Viola Jones algorithm and the VGG16. The purpose for which these methods are used is discussed and the results observed by using these methods is also discussed. This survey paper describes and compares the various techniques of mainly object detection and face detection. A conclusion on what method will be suitable for certain object detection or face detection methods can be formed by studying this paper. The survey presented is helpful in identifying the strong points and limitations of the different popular methods used nowadays.
A SYSTEMATIC LITERATURE MAPPING ON SECURE IDENTITY MANAGEMENT USING BLOCKCHAIN TECHNOLOGY

Although a safe exchange of information, services, and technologies are possible via centralized digital organizations, there are significant concerns associated with the digital revolution. Without the users' knowledge or permission, telecommunications companies gather and absorb information that is utilized for data analysis, profiling, and exploiting purposes by third parties [1]. The continuation of service providers' centered solutions is wasteful due to duplication, it has significant security flaws, and it is burdensome for consumers. To accomplish the security and privacy of dispersed digital identities, it is necessary to authenticate and verify the digital individual's identity [1]. Nevertheless, thorough research on the elements of identity management, as well as user data protection and privacy measures in the identity management system, is still lacking in the published studies, according to the researchers [1,2]. Managing and verifying digital identities is undoubtedly among the most important applications of blockchain technology for various developments. Throughout 2018, millions of individuals all around the globe were affected by data breaches involving their personal information. It is necessary to develop more secure methods of storing, exchanging, and validating sensitive material. In this respect, developing blockchain solutions for identification systems may help address some of the issues experienced by most centralized databases.
Multi-Source Domain Adaptation via Optimal Transport for Brain Dementia Identification

Multi-site MRI data have been increasingly employed for automated identification of brain dementia, but are susceptible to large domain shift between different imaging sites/centers. Previous studies usually simply ignore the domain shift caused for instance by different scanners/protocols. Even though several studies proposed to reduce inter-domain discrepancy, they generally require a part of labeled target data and cannot well handle problems with multi-source domains. To this end, we propose a multi-source optimal transport (MSOT) framework for cross-domain Alzheimer's disease (AD) diagnosis with multi-site MRI data. Specifically, we first project data from multi-source domains to target domain through optimal transport in an unsupervised manner. Based on projected representation, we calculate the similarity between each source and target domains, and use this similarity as the source domain weight. We then train a support vector machine (SVM) classifier based on projected samples from each source domain. Finally, an ensemble learning strategy via weighted voting is used to predict labels of target samples. The proposed MSOT does not require labeled target data and can be efficiently optimized. Experiments were performed on three benchmark neuroimaging datasets for AD identification, with results suggesting the superiority of MSOT over several state-of-the-art methods.
Teamwork quality in technology‐driven product teams in large‐scale agile organizations

Abstract The maturity of teams is related to the specific solutions they are building and maintaining. Every team needs generic skills and capabilities for successful teamwork. However, depending on the used technology, particular skills and qualifications are required to form a mature team to handle the solution‐related products and services adequately. Especially in agile organizations, autonomous teams have the responsibility for their solutions and their team development. To foster transparency and structured team development, the Technical Team Maturity (TTM) approach is presented. The TTM approach shapes and models specific technologies, for example, cloud technology demands about team skills and capabilities to form a reliable delivery team. The approach is divided into a technology‐specific profile and an evaluation model to support active usage in teams. The profile focuses on abstract and generic technology aspects. The evaluation model validates against the profile with a state‐of‐the‐art focus about the technology application to determine the maturity. The different maturity levels are mapped to belts—as a gamification aspect to foster continuous improvement in the teams with this benchmark. TTM is evaluated in the enterprise context of Volkswagen AG and Volkswagen Financial Services AG. It provides insights into the effectiveness of this approach in heterogeneous multiteam settings and its strengths, weaknesses, and limitations.
Privacy protection of medical data in social network

Abstract Background Protection of privacy data published in the health care field is an important research field. The Health Insurance Portability and Accountability Act (HIPAA) in the USA is the current legislation for privacy protection. However, the Institute of Medicine Committee on Health Research and the Privacy of Health Information recently concluded that HIPAA cannot adequately safeguard the privacy, while at the same time researchers cannot use the medical data for effective researches. Therefore, more effective privacy protection methods are urgently needed to ensure the security of released medical data. Methods Privacy protection methods based on clustering are the methods and algorithms to ensure that the published data remains useful and protected. In this paper, we first analyzed the importance of the key attributes of medical data in the social network. According to the attribute function and the main objective of privacy protection, the attribute information was divided into three categories. We then proposed an algorithm based on greedy clustering to group the data points according to the attributes and the connective information of the nodes in the published social network. Finally, we analyzed the loss of information during the procedure of clustering, and evaluated the proposed approach with respect to classification accuracy and information loss rates on a medical dataset. Results The associated social network of a medical dataset was analyzed for privacy preservation. We evaluated the values of generalization loss and structure loss for different values of k and a , i.e. $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> = {3, 6, 9, 12, 15, 18, 21, 24, 27, 30}, a = {0, 0.2, 0.4, 0.6, 0.8, 1}. The experimental results in our proposed approach showed that the generalization loss approached optimal when a = 1 and k = 21, and structure loss approached optimal when a = 0.4 and k = 3. Conclusion We showed the importance of the attributes and the structure of the released health data in privacy preservation. Our method achieved better results of privacy preservation in social network by optimizing generalization loss and structure loss. The proposed method to evaluate loss obtained a balance between the data availability and the risk of privacy leakage.
Partial Variable Training for Efficient on-Device Federated Learning

This paper aims to address the major challenges of Federated Learning (FL) on edge devices: limited memory and expensive communication. We propose a novel method, called Partial Variable Training (PVT), that only trains a small subset of variables on edge devices to reduce memory usage and communication cost. With PVT, we show that network accuracy can be maintained by utilizing more local training steps and devices, which is favorable for FL involving a large population of devices. According to our experiments on two state-of-the-art neural networks for speech recognition and two different datasets, PVT can reduce memory usage by up to 1.9× and communication cost by up to 593× while attaining comparable accuracy when compared with full network training.
Implementation and evaluation of a privacy-preserving distributed ABC scheme based on multi-signatures

Despite the latest efforts to foster the adoption of privacy-enhancing Attribute-Based Credential (p-ABC) systems in electronic services, those systems are not yet broadly adopted. The main reasons behind this are performance efficiency issues, lack of interoperability with standards, and the centralized architectural scheme that relies on a unique Identity Provider (IdP) for credential issuance. To cope with these limitations, this paper describes the first implementation of the Pointcheval–Sanders Multi-Signatures (PS-MS) crypto scheme proposed by Camenisch et al. and its integration in a distributed and privacy-preserving identity management system proposed in OLYMPUS H2020 European research project. Our efficient implementation provides remarkable privacy-preservation features for identity management in online transactions leveraging p-ABC systems, including unforgeability, minimal disclosure of personal data through zero-knowledge proofs, unlinkability in online transactions and fully distributed credential issuance across different IdPs, thereby removing the IdP as a unique point of failure. The performance of the implementation has been exhaustively analyzed and evaluated with different curves, signers and number of attributes, and compared against Identity Mixer, the best known p-ABC system, outperforming significantly the credential issuance and zero-knowledge proving and verification processes (2–4 times less execution time).