Reviewerly/exHarmony huggingface dataset:
An Identity-based Group Signature Approach on Decentralized System and Chinese Cryptographic SM2

While reducing costs and improving data security, the new generation of informatics technologies such as blockchain also face problems of operation efficiency and privacy leakage, which have attracted extensive attention from researchers. Digital signature is one of the key technologies to solve the above problems. The group signature algorithm has the dual characteristics of protecting the privacy of signer identity and tracing effectively when disputes occur. The scheme we proposed can simultaneously solve the low efficiency of signature verification caused by the high time-consuming bilinear pairwise operation in existing group signature algorithms and the privacy leakage of signers caused by the vulnerability of single group administrators to malicious attacks. Compared with the SM2 digital signature algorithm of Chinese cryptographic standard, the proposed scheme increases the signature anonymization while maintaining the same signature and verification efficiency as the SM2 signature algorithm. Compared with Yang et al. 's scheme, the main computation overhead and communication bandwidth of the proposed protocol are significantly reduced. Therefore, the design scheme in this paper has stronger practicability and is more suitable for scenarios that require both efficiency and strong privacy protection, such as blockchain, anonymous certificate, electronic cash and electronic voting.
Construction of Nonlinear Optimal Diffusion Functions over Finite Fields

The diffusion function with large branch number is a fundamental building block in the construction of many block ciphers to achieve provable bounds against differential and linear cryptanalysis. Conventional diffusion functions, which are constructed based on linear error-correction code, has the undesirable side effect that a linear diffusion function by itself is “transparent” (i.e., has transition probability of 1) to differential and linear cryptanalysis. Nonlinear diffusion functions are less studied in cryptographic literature, up to now. In this paper, we propose a practical criterion for nonlinear optimal diffusion functions. Using this criterion we construct generally a class of nonlinear optimal diffusion functions over finite field. Unlike the previous constructions, our functions are non-linear, and thus they can provide enhanced protection against differential and linear cryptanalysis.
A Survey of Hand Gesture Recognition Based on FMCW Radar

In recent years, with the development of unmanned vehicle fields, the millimeter wave radar has also developed rapidly and has been applied to many systems. Frequency modulated continuous wave (FMCW) radar has become a hot research topic in millimeter radar areas due to its physical characteristics, including strong sensing capability and high resolution. This paper investigates gesture recognition applications based on FMCW radar and summarizes the latest research using FMCW radar system. Firstly, this paper reviews existing gesture recognition applications using wireless signals. Secondly, it focuses on the FMCW radar gesture recognition system and gives the general framework of gesture recognition, including gesture data acquisition, signal preprocessing, gesture recognition algorithm and classification results. Next, it analyzes the typical gesture recognition application systems from coarse-grained and fine-grained granularity and elaborates experimental scenes, experimental equipment, gesture types, signal preprocessing and classification methods. Finally, it presents the challenges and issues involved in gesture recognition based on FMCW radar and proposes future research directions.
Multi-view 3D Human Physique Dataset Construction For Robust Digital Human Modeling of Natural Scenes

A large number of diverse data sets are necessary for networks to predict human body parameters and reconstruct 3D body models from images. Due to the high cost of motion capture and body scanning, high precision pose and body shape parameters are difficult to obtain. Meanwhile, existing datasets cannot meet the requirements in terms of diversity, size, and data accuracy for practical applications. Inspired by the construction schemes of various datasets, we design and construct a large multi-view 3D human body reconstruction dataset (3DMVHumanBP) with more types of supervised data. By recording the different poses of 25 women and 25 men in a green screen laboratory from six perspectives, we constructed a complete large multi-view 3D body posture dataset containing 340, 000 images. It is worth noting that, we innovatively propose a body dimension prior to the constrained human parametric model construction strategy to provide high-precision ground truth parameters of the human body SMPL models. In addition, we also designed a dense UV data generation method based on human body boundary and mask mapping to provide high-quality dense UV data, which more closely fits the features of the human images. It makes up for the defect that few existing data sets can only provide sparse UV data. In the experiment, the effectiveness and advantages of the data set constructed by us in network training are verified. Compared with the training of existing datasets, the mainstream network models trained on our datasets can significantly improve their prediction accuracy and robustness, thanks to the monitoring data of multiple kinds of high-precision human model parameters provided by 3DMVHumanBP. We hope that the human body dataset construction scheme we designed can provide ideas for building large-scale high precision human body datasets in the future.
Depth Estimation from a Single Image Using Line Segments only

We present a method for depth estimation from a single image using an intermediate representation in the form of line segments. Rather than regressing depth from a chromatic image in RGB format, we explore the use an image containing line segments extracted from the original chromatic image using the Line Segment Detector (LSD), arguing that this image, even when sparse in visual data, still contains information to infer a depth image. Our proposed approach has been tested on the NYU-depth dataset for indoor scenes and on simulated images created with Airsim, seeking to assess the performance of our method with synthetic images. Our experiments show promising results confirming that it is possible to estimate a depth image from a single image containing line segments only.
Semi-supervised Hierarchical Classification Based on Local Information

In this work, a semi-supervised hierarchical classifier based on local information (SSHC-BLI) is proposed. SSHC-BLI is a semi-supervised learning algorithm that can be applied to hierarchical classification, that is, it can handle labeled and unlabeled data in scenarios where the labels are arranged in a hierarchical structure. SSHC-BLI tries to pseudo-label each unlabeled instance using information of its nearest labeled instances. It uses a similarity function to determine whether the unlabeled instance is similar to its nearest labeled instances to assign it a label; if it is not, then it continues unlabeled. A heuristic similarity function of an instance with a set of instances was proposed to determine similitude. The method was tested in several datasets from functional genomics and compared against a hierarchical supervised classifier and two state of the art methods, showing in most cases superior performance, with statistical significance in accuracy and hierarchical F-measure.
Antonymy-Synonymy Discrimination in Spanish with a Parasiamese Network

Antonymy-Synonymy Discrimination (ASD) is a challenging NLP task that has been tackled mainly for English. We present a dataset for ASD in Spanish, built using online dictionaries and Wordnet in Spanish. To evaluate the quality of the dataset, we performed two manual annotations on a random sample of the dataset, obtaining 0.89 of kappa score between them. Additionally, each annotator obtained 0.9 of agreement according to the dataset. The dataset is split into train, val and test using three algorithms: (1) randomly, (2) Shwartz’s split [20], and (3) graph split. The last two are without lexical intersection between its parts, and (3) is based on the topology of the antonymy relation depicted from data. Finally, we report results using a parasiamese neural network [6] in our dataset, one of the best performing approaches in the literature.
Model Compression for Deep Reinforcement Learning Through Mutual Information

One of the most important limitation of deep learning and deep reinforcement learning, is the number of parameters in their models (dozens to hundreds of millions). Different model compression techniques, such as policy distillation, have been proposed to alleviate this limitation. However, they need a high number of instances to obtain acceptable performance and the use of the source model. In this work, we propose a model compression method based on the comparison of mutual information between the distribution layers of the network. This method automatically determines how much the model should be reduced, and the number of instances required to obtain acceptable performance is considerably lower than the state-of-the-art solutions (19M). It also requires lower resources because only the last two layers of the network are fine-tuned.
The Underlying Technology for Cryptoassets

This chapter reviews the underlying technologies of cryptoassets, including fundamental cryptographic primitives used in distributed ledger technologies and permissionless blockchain technologies and their consensus protocols such as proof-of-work and proof-of-stake. It discusses the pros and cons of existing approaches to improve blockchain scalability and considers the requirements for security and decentralization. The chapter also examines the following techniques: layer 1 tuning, layer 1 sharding, and layer 2 solutions. It concludes with an overview of technologies to swap cryptoassets off-chain, technical requirements for cross-chain transactions, and reviews cross-chain atomic swap implementation using hashed time lock contracts.
COVID-19: Introducing a sliding scale between legality and scientific knowledge

Abstract This article aims to explore the new normal in lawmaking during the COVID-19 pandemic. It proves how the pandemic has affected the making of legal norms, in terms of both process and content. It argues that COVID-19 legislation is largely driven by scientific data for the sake of public health. In this context, it explains how national-decision making is influenced by expert advisory bodies that attempt to specify how public health may be preserved during a pandemic crisis. Moreover, it sheds light into the fact that law-making during the first phases of the pandemic was approved and endorsed by the populations of states, due to their fear of the unknown disease. However, as the pandemic steadily became an established truth, the public’s trust in lawmaking started to decrease. These shifts are well explained if one conceives lawmaking by expertise as a sliding scale, the ends of which are legality at one end and expertise coupled with popular acceptance at the other. This unique sliding scale depicts how COVID-19 lawmaking functioned, balancing between opposite trends.
A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise

The outbreak of COVID-19 on the Diamond Princess cruise ship has attracted much attention. Motivated by the PCR testing data on the Diamond Princess, we propose a novel cure mixture nonparametric model to investigate the detection pattern. It combines a logistic regression for the probability of susceptible subjects with a nonparametric distribution for the detection of infected individuals. Maximum likelihood estimators are proposed. The resulting estimators are shown to be consistent and asymptotically normal. Simulation studies demonstrate that the proposed approach is appropriate for practical use. Finally, we apply the proposed method to PCR testing data on the Diamond Princess to show its practical utility.
Graph-Based Replication and Two Factor Authentication in Cloud Computing

Many cutting-edge methods are now possible in real-time commercial settings and are growing in popularity on cloud platforms.By incorporating new, cutting-edge technologies to a larger extent without using more infrastructures, the information technology platform is anticipating a completely new level of development.The following concepts are proposed in this research paper: 1) A reliable authentication method Data replication that is optimised; graph-based data encryption and packing colouring in Redundant Array of Independent Disks (RAID) storage.At the data centre, data is encrypted using crypto keys called Key Streams.These keys are produced using the packing colouring method in the web graph's jump graph.In order to achieve space efficiency, the replication is carried out on optimised many servers employing packing colours.It would be thought that more connections would provide better authentication.This study provides an innovative architecture with robust security, enhanced authentication, and low cost.
Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years.Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae.Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients.Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms.A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases.Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques.A dataset acquired from the 'Drugs.Com' containing reviews of drug-related side effects and reactions, is used for experiments.A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text.Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network.The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level.Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods.Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy.We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.
Convolutional Deep Belief Network Based Short Text Classification on Arabic Corpus

With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides, the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last, the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model, a wide range of simulations have been performed.The simulation results confirmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.
Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application.When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained.In addition, every workload or application required for the framework is characteristic and these essentials change over time.But, the existing method was failed to ensure the high Quality of Service (QoS).In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced.A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server.Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods.
Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations.This necessitates the medical field to be redesigned by cloud technology to preserve information about patient's critical diseases, electrocardiogram (ECG) reports, and payment details.The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud.The stored data are categorized as significant and non-significant by Artificial Neural Networks (ANN).The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer.Upon receiving the request from a secondary user, the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user.Once the key matches the key in the hidden layer, the preserved information will be shared between the users.Due to the enhanced privacy preserving key generation, the proposed work prevents the tracking of keys by malicious users.The outcomes reveal that the introduced work provides improved success rate with reduced computational time.
A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment, consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand, Cloud Service Providers (CSPs) seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers' needs or CSPs' capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making (MCDM) method, called Analytic Hierarchy Process (AHP), to acquire the information of consumers' preferences and service providers' capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria, and matching each set of tasks/services to the best CSPS.
Usability-Driven Mobile Application Development

Recently, a specific interest is being taken in the development of mobile application (app) via Model-Based User Interface Development (MBUID) approach.MBUID allows the generation of mobile apps in the target platform(s) from conceptual models.As such it simplified the development process of mobile app.However, the interest is only focused on the functional aspects of the mobile app while neglecting the non-functional aspects, such as usability.The latter is largely considered as the main factor leading to the success or failure of any software system.This paper aims at addressing non-functional aspects of mobile apps generated using MBUID approach.As such, we propose a usability-driven approach for the development of mobile apps.The main stages of the proposed approach are defined in a generic way so that they can be integrated with any MBUID method.A case study is presented, in the paper, with the aim of illustrating the feasibility of this approach.