Reviewerly/exHarmony huggingface dataset:
Accurate Hand Gesture Recognition using CNN and RNN Approaches

Interaction between humans and computers, gesture recognit ion does play a critical role.By using modified convolutional neural network (CNN) and modified Recurrent Neural Networks (RNN) models for hand posture estimation, hand motion capture and hand object interaction in hand gesture recognition.By these models, we performed hand pose estimation on ICVL data set, motion capture available postures dataset and hand object interaction on kaggle dataset.RNN is for estimation and CNN is for feature extraction.A comparison of those three modules hand motion capture gives better accuracy.
Aprendizaje basado en simulación con realidad virtual

The use of virtual reality (VR) and augmented reality (AR) to improve learning is a topical issue in Higher Education. This paper aims to assess the impact of virtual reality simulation (VR-SBL) on student learning and satisfaction. To this end, two cases are presented that have been carried out at Universidad Europea de Madrid (UEM): the first consists of training experience to provide first aid in the event of a traffic emergency through the use of immersive virtual reality technology, where a comparison has been made between how students learn with this new methodology concerning the traditional master class. The second case is the simulation of accidents in a laboratory using virtual reality desktop technology, where a comparison has been made between how students learn with this methodology for simulation of such accidents in the real laboratory. In both cases, a quasi-experimental design with an experimental group and a control group has been used. Also, a pretest-posttest design has been applied, consisting of a knowledge test before the experience (pretest) and a knowledge questionnaire after the experience (posttest). To measure satisfaction with the activity and the adequacy of the tools used, a user experience questionnaire was administered. In conclusion, student satisfaction was high in both cases. Besides, in the first case, the experimental group using immersive virtual reality had a significantly better academic performance. As for the second case, no significant differences were found in terms of learning. Therefore, the level of learning in both groups (virtual reality simulation and real scenario simulation) was the same.
Chinese Implicit Sentiment Analysis Based on Hierarchical Knowledge Enhancement and Multi-Pooling

Sentiment analysis is one of the significant application fields of natural language processing.Researchers divide the sentiment into explicit sentiment and implicit sentiment according to whether the expression contains sentiment words or not.Most of the existing research focuses on explicit sentiment tasks.However, implicit sentiment analysis is difficult to study because of its implicit tendency to express sentiment and the problem of ''weak features'' and ''multi-confounding weak features''.We develop a novel model based on ''Hierarchical Knowledge Enhancement and Multi-pooling''(HKEM ), which fully integrates the knowledge information of different levels in the text through hierarchical knowledge enhancement to alleviate the ''weak feature'' problem.At the same time, the multi-pooling method is used to extract and analyze multiple features, and the ''multi-confounding weak features'' are modeled.In this paper, comparative experiments have been conducted on the SMP-ECISA2019 dataset, and the results show that the accuracy of this model is comparable to that of the current best model, and the F1 score is 5.9% higher than that of the best model, indicating that the model proposed in this paper can accurately analyze Chinese implicit sentiment and achieve the state-of-the-art in the comprehensive performance evaluation, which proves the effectiveness and superiority of the model in this paper.
Does competence of central bank governors influence financial stability?

Abstract This study investigates whether the competence of central bank governors affects the stability of the financial system they are responsible for. Using publicly available information about central bank governors from 2000 to 2016 together with data on financial stability and the macroeconomy, the findings reveal that central bank governors’ competence promotes financial stability, depending on how competence is measured. Specifically, the findings reveal that the financial system is more stable when the central bank governor is older and male. The financial system is also stable during the tenure of a central bank governor that has a combination of cognitive ability, social capital and technical competence in economics. The gender analyses reveal that the financial system is also stable during the tenure of a female central bank governor that has high social capital or high cognitive abilities while the financial system is relatively less stable during the tenure of a male central bank governor that has high social capital or high cognitive abilities. Comparing developed countries to developing and transition countries, the findings reveal that the financial system of developed countries is more stable during the tenure of a central bank governor that has high cognitive ability, social capital and technical competence in economics while the financial system of developing and transition countries is less stable during the tenure of a central bank governor that has high cognitive ability, social capital and technical competence in economics. Also, there is evidence that the financial system of developing and transition countries is more stable during the tenure of a central bank governor that has knowledge in disciplines other than economics. The findings are consistent with the view that certain characteristics of central bankers shape their beliefs, preferences and choice of policy, which in turn, are consequential for policy outcomes during their tenure.
Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization

Orthogonal Frequency Division Multiplexing (OFDM), as the core technology in mobile communications, is a multi-carrier modulation technology with high frequency spectrum utilization, which has strong anti-multipath interference and anti-fading ability. The significant advantage of OFDM signals lies in the anti-multipath effect, so its application environment is mostly multipath fading channels. Therefore, it is of great significance to study the identification of OFDM signals in multipath channels. Deep learning, with superior big data processing and classification capabilities, is a potential solution to these problems. Based on the problem of OFDM signal recognition in complex signals in multi-path channel, an OFDM signal recognition method based on hybrid grey wolf optimization algorithm to optimize deep neural network model is proposed. Because the basic grey wolf optimization algorithm (GWO) is easy to fall into a stasis state when attacking prey, differential evolution algorithm (differential evolution algorithm) is integrated into GWO to force GWO to jump out of the stasis state with its strong search ability. The convergence speed and recognition performance of the proposed algorithm are greatly improved. The experimental results show that under the condition of low SNR, the recognition accuracy of proposed algorithm is 9.95% higher than the traditional DNN method, and nearly 4.5% higher than the other two intelligent optimization methods, and the values of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precision</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall</i> increase obviously, which indicates that the hybrid algorithm not only improves the accuracy of recognition, but also makes the search more complete and accurate. Compared with classical particle swarm optimization (PSO) and whale algorithm optimization algorithm (WOA), the hybrid algorithm has strong competitiveness both in recognition performance and optimization stability, which provides a new, simpler and more effective method for modulation recognition of OFDM signals in wireless communications.
Distributing tobacco in the dark: assessing the regional structure and shipping patterns of illicit tobacco in cryptomarkets

The size of the global market for illicit tobacco products is estimated to be between USD$8.6 and USD$11.6 billion yearly. In addition to an estimated cost of USD$40.5 billion in lost tax revenue the illicit tobacco market further increases the accessibility of a harmful substance for minors and provides a revenue stream for both organised crime and violent political groups. In this paper, we examine how tobacco products are distributed globally through illicit online platform economies known as cryptomarkets. Using data from the cryptomarket Empire, we find tobacco products remain a small niche market exclusively shipping from the EU and that shipping patterns suggest the emergence of new supply routes for end-consumers within Western Europe originating from the UK. We find that the market for tobacco on cryptomarkets remains minimal, as in previous research, compared to the market for drugs.
Application of Fog Computing, Internet of Things, and Blockchain Technology in Healthcare Industry

As healthcare industry is growing the major concerns are storage of healthcare data including medical and nonmedical data, accessing of data, and data security. The healthcare sector is one of the fastest developing sectors that focus on medical and nonmedical entities of the system like patients and doctors, medical equipment and drugs manufacturers, medical insurance facilities providers, etc. In parallel, it also includes multiple sectors. This chapter discussed the amalgamation of fog computing, blockchain, and Internet of Things (IoT) in healthcare. Fog computing extends the capability of cloud computing that works between the cloud and end user devices called IoT devices to perform operations such as computation, storage, and communication over the Internet. It provides better data storage facilities with real-time access, lower latency, higher response, better fault tolerance, secure and conceal environment. In IoT, conglomerate devices are interconnected and fragments IoT system into five layers such as fog, access, data interface, application, and security layers. To provide better security of the data in healthcare environment, we discussed blockchain technology and consensus mechanism. This research focuses on the usefulness of technologies for existing patients and normal users and improves the services of healthcare industry.
A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines

The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis.
Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy

Zooplankton is enormously diverse and fundamental group of microorganisms that exists in almost every freshwater body, determining its ecology and play a vital role in food chain.Considering the significance of zooplankton, the study of freshwater zooplankton is very essential which intensely relies on the classification of images.However, the routine manual analysis and classification is laborious, time consuming and expensive, and poses a significant challenge to experts.Thus, for recent decade much research is focused on the development of underwater imaging technologies and intelligent classification system of zooplankton.This work presents devotion to observation of freshwater zooplankton by designed underwater microscope and modeling the system for automatic classification among four different taxa.Unlike most of the existing zooplankton image classification systems, this model is trained on a comparatively small dataset collected from freshwater by designed underwater microscope.Transfer learning of pretrained AlexNet Convolutional Neural Network (CNN) model proved to be a potential approach in the system design.Among four networks trained over two datasets, the best overall classification accuracy of up to 93.1%, comparable to other existing systems was achieved on test dataset (92.5% for Calanoid and Cyclopoid (Female), 90% for Cyclopoid (Male) and 97.5% for Daphnia).Graphical User Interface (GUI) of the model constructed on MATLAB, makes it easy for the users to collect images for building database, train network and to classify images of different taxa.Moreover, the designed system is adaptable to the addition of more classes in the future.
A Three-Level Recursive Differential Grouping Method for Large-Scale Continuous Optimization

Cooperative co-evolution (CC) is widely used to solve large-scale continuous optimization problems, which divides a large-scale problem into several small-scale sub-problems via decomposition methods and then optimizes each sub-problem separately. However, the performance of CC mainly depends on the decomposition methods. A recently proposed bisection-based decomposition method, called recursive differential grouping (RDG), shows good performance when solving large-scale continuous optimization problems. In order to further improve the performance of RDG, this paper develops a novel decomposition method, called three-level recursive differential grouping (TRDG). In TRDG, when the interaction between two sets is detected, the variables in one of the sets are divided into three subsets based on the trichotomy method, and then the interaction between each subset and the other set is detected. Compared with RDG, TRDG can reduce the depth of recursion, thus saving the number of fitness evaluations (FEs). In addition, we devise a novel strategy to update adaptively the threshold for identifying the interactions between variables. The simulation experiment results on CEC'2010 and CEC'2013 benchmark functions show that the performance of TRDG is better than several existing decomposition methods in terms of the accuracy and the number of FEs. Furthermore, TRDG is embedded into two frameworks to tackle CEC'2010 large-scale continuous optimization problems.
Soft Autoencoder and Its Wavelet Adaptation Interpretation

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this article, we propose a new type of convolutional autoencoders, termed as Soft Autoencoder (Soft-AE), in which the activation functions of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized with linear units. Consequently, Soft-AE can be naturally interpreted as a learned cascaded wavelet shrinkage system. Our denoising experiments demonstrate that Soft-AE not only is interpretable but also offers a competitive performance relative to its counterparts. Furthermore, we propose a generalized linear unit (GenLU) to make an autoencoder more adaptive in nonlinearly filtering images and data, such as denoising and deblurring.
Credit Card Fraud Detection using Machine Learning Algorithms

Now a days Using technology like phishing technique to do internet banking fraud means transferring and removing the money from banker account without the permission of the banker.credit card frauds are happening in large amount and also some banking companies and the companies giving service to banks are facing problem.In this project trying build the model which predict fraud and nonfraud transaction in best way using machine learning algorithms and neural networks.Objective of the project is to predict the fraud and fraud less transaction with respect to the time and amount of the transaction using classification machine learning algorithms and statistics and calculus (differentiation ,chain rule etc) and linear algebra in building of the complex machine learning models for prediction and understanding of the data set .we have achieved accuracy of 94.84% using logistic regression and 91.62% using naive Bayes and 92.88% using decision tree and we step into deep learning, we used ANN achieved better accuracy then all other algorithms of 98.69%.
Semi-Supervised Node Classification With Discriminable Squeeze Excitation Graph Convolutional Networks

In recent years, Graph Convolutional Networks (GCNs) have been increasingly and widely used in graph data representation and semi-supervised learning. GCNs can reveal and dig deep into irregular data with spatial topological structure. However, in the task of node classification, most models will be over-smoothing (indistinguishable representations of nodes in different classes) after stacking multilayer GCNs. To alleviate the issue of over-smoothing, we propose a novel Discriminable Squeeze and Excitation Graph Convolutional Network (D-SEGCN) based on the attention mechanism of features for semi-supervised node classification. In the proposed D-SEGCN model, Squeeze and Excitation (SE) module is fused to the Graph Convolutional Networks to form SEGCN, which can selectively enhance significant features and realize the adaptive calibration of feature dimensions, thus enhancing node features. Then, the feature representation is obtained through a hierarchical structure, it is put into a discriminable capsule layer which can judge the similarities between the node features to obtain new feature. Finally, the feature representation and new feature are fused to obtain the final feature to strengthen the discriminability of nodes. Furthermore, we demonstrate that D-SEGCN significantly outperforms the state-of-the-art methods on three citation networks datasets.
Solving Rolling Shutter 3D Vision Problems using Analogies with Non-rigidity

We propose an original approach to absolute pose and structure-from-motion (SfM) which handles rolling shutter (RS) effects. Unlike most existing methods which either augment global shutter projection with velocity parameters or impose continuous time and motion through pose interpolation, we use local differential constraints. These are established by drawing analogies with non-rigid 3D vision techniques, namely shape-from-template and non-rigid SfM (NRSfM). The proposed idea is to interpret the images of a rigid surface acquired by a moving RS camera as those of a virtually deformed surface taken by a GS camera. These virtually deformed surfaces are first recovered by relaxing the RS constraint using SfT or NRSfM. Then we upgrade the virtually deformed surface to the actual rigid structure and compute the camera pose and ego-motion by reintroducing the RS constraint. This uses a new 3D-3D registration procedure that minimizes a cost function based on the Euclidean 3D point distance. This is more stable and physically meaningful than the reprojection error or the algebraic distance used in previous work. Experimental results obtained with synthetic and real data show that the proposed methods outperform existing ones in terms of accuracy and stability, even in the known critical configurations.
Are Smart Contracts and Blockchains Suitable for Decentralized Railway Control?

&#x0D; &#x0D; &#x0D; Conventional railway operations employ specialized software and hardware to ensure safe and secure train operations. Track occupation and signaling are governed by central control offices, while trains (and their drivers) receive instructions. To make this setup more dynamic, the train operations can be decentralized by enabling the trains to find routes and make decisions which are safeguarded and protocolled in an auditable manner. In this paper, we present the case study findings of a first-of-its-kind blockchain-based prototype implementation for railway control, based on decentralization but also ensuring that the overall system state remains conflict-free and safe. We also show how a blockchain-based approach simplifies usage billing and enables a train-to-train/machine-to-machine economy. Finally, first ideas addressing the use of blockchain technology as a life-cycle approach for condition-based monitoring and predictive maintenance in train operations are outlined.&#x0D; &#x0D; &#x0D;
Dynamic Camera Reconfiguration with Reinforcement Learning and Stochastic Methods for Crowd Surveillance

Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras.