Reviewerly/exHarmony huggingface dataset:
A study of the economic impact of central bank digital currency under global competition

From both theoretical and practical perspectives, we examine the global development and competition of digital currencies, and investigate the design of China's central bank digital currency (CBDC). Moreover, on the basis of correcting shortcomings in the existing literature, we undertake a quantitative analysis of the economic impact of the issuance of DC/EP based on a four-sector DSGE model. The results demonstrate that the substitution effect of DC/EP on bank deposits is limited, while the unit impact can enhance the economic growth rate by 0.15% and the overall economic effect is positive, at the same time it reduces the leverage ratio to a certain degree, which is conducive to reducing systemic financial risk. Therefore, we contend that China should accelerate the research and development of DC/EP and launch pilot schemes to promote DC/EP. Moreover, China should actively participate in the drafting of international regulations for digital currencies, selectively liberalize the jurisdiction of overseas nodes, jointly establish an integrated digital infrastructure for future generations.
A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques

The world is being more and more digitalized with each passing day, making information security a huge concern. Through efficient fraud detection using a robust approach, the banking industry will be able to avert fraudulent transactions and save millions of dollars every year. Each money transaction is crucial and thus, fraudulent transactions need to be detected at any cost. In this paper, we build models to detect fraudulent credit card transactions using five classifiers to find out the best fit classifier for the situation. We use two different techniques to tackle the inherent problem of data imbalance. The first technique uses data resampling technique to increase the number of samples in the minority class, whereas, the second one uses a cost-based approach where the error function incorporates weights of each class. Through the weights, more emphasis can be given to the samples of fraudulent transactions than the normal samples.
A Spam Email Detection Mechanism for English Language Text Emails Using Deep Learning Approach

Phishing emails are emails that pretend to be from a trusted company that target users to provide personal or financial information. Sometimes, they include links that may download malicious software on user's computers, when clicked. Such emails are easily detected by spam filters that classify any email with a link as a phishing email. However, emails that have no links, link-less emails, requires more effort from the spam filters. Although many researches have been done on this topic, spam filters are still classifying some benign emails as phishing and vice-versa. This paper is focused on classifying link-less emails using machine learning approach, deep neural networks. Deep neural networks differs from simple neural network by having multiple hidden layers where data must be processed before reaching the output layer. The data used in this research is publicly available online. Hyper parameter optimization, was performed, using different settings on the data. In order to demonstrate the effectiveness of the approach, precision, recall and accuracy were computed. The results show that the deep neural network performed well in many of its settings.
A hybridized multi-algorithm strategy for engineering optimization problems

This paper presents a new multi-hybrid algorithm (MHA) based on the hybridization concepts incorporated in the naked mole-rat algorithm (NMRA). The new algorithm uses the exploratory concepts of whale optimization algorithm (WOA), moth flame optimization (MFO) and marine predator algorithm (MPA) to enhance the worker phase or the exploration operation in NMRA. In order to overcome the problem of local optima stagnation, a new stagnation phase inspired from the concepts of GWO and cuckoo search (CS) algorithm is also added. Apart from these modifications, self-adaptivity has been incorporated by adapting all the parameters of the proposed algorithm. Here five new mutation strategies including simulated annealing (sa), exponentially decreasing (exp), chaos based (chaotic), linearly decreasing (linear) and oscillatory inertia weight (oscillating) have been exploited for different parameters of all the algorithms under study so that no user based parametric adjustments are required. For performance evaluation, the MHA is subjected to CEC 2005, CEC 2014 benchmark problems and image thresholding problem. The comparison on variable population sizes and dimension size has been done to prove the significance of the proposed algorithm over variable function evaluations and higher dimensional problems. From the experimental results, it can be seen that proposed MHA performs better than adaptive differential evolution with optional external archive (JADE), self adaptive differential evolution (SaDE), opposition and exponential whale optimization algorithm (OEWOA), sine cosine crow search algorithm (SCCSA), fractional-order calculus-based flower pollination algorithm (FA-FPO), success-history based parameter adaption differential evolution (SHADE), SHADE with linear population size reduction hybrid with semi-parameter adaptation of CMA-ES (LSHADE-SPACMA), Laplacian biogeography-based optimization (LX-BBO), random walk grey wolf optimizer (RW-GWO), improved symbiotic organisms search (ISOS), variable neighbourhood bat algorithm (VNBA), improved elephant herding optimization (IMEHO) and others. Statistical results in terms of Wilcoxon’s rank-sum test, Freidman’s test and convergence profiles further validate the superior performance of the proposed MHA algorithm. The source code can be downloaded from https://github.com/rohitsalgotra.
A cluster-based intelligence ensemble learning method for classification problems

Classification is a vital task in machine learning. By learning patterns of samples of known categories, the model can develop the ability to distinguish the categories of samples of unknown categories. Noticing the advantages of the clustering method in cluster structure analysis, we combine the clustering and classification methods to develop the novel cluster-based intelligence ensemble learning (CIEL) method. We use the clustering method to analyze the inherent distribution of the data and divide all the samples into clusters according to the characteristics of the dataset. Then, for each specific cluster, we use different classification algorithms to establish the corresponding classification model. Finally, we integrate the prediction results of each base classifier to form the final prediction result. In view of the problem of parameter sensitivity, we use a swarm intelligence algorithm to optimize the key parameters involved in the clustering, classification, and ensemble stages in order to boost the classification performance. To assess the effectiveness of CIEL, we perform tenfold cross-validation experiments on the 24 benchmark datasets provided by UCI and KEEL. Designed to improve the performance of the classifiers, CIEL outperforms other popular machine learning methods such as naive Bayes, k-nearest neighbors, random forest, and support vector machine.
RAU-Net: U-Net Model Based on Residual and Attention for Kidney and Kidney Tumor Segmentation

Various variants based on U-Net model have made great achievements in various medical image segmentation competitions, however their ability to generalize is less than satisfactory. Therefore, RAU-Net, our proposed model is used for renal tumors segmentation. To improve the performance of the model, the work can be summarized as the following four points: Above all, we have proposed an end-to-end automatic segmentation model, which combined with residual and attention, and allowed us to obtain the kidney and kidney tumor just by preconditioning. Second, the weighted dice loss function and the cross entropy loss function enable the model to fully identify the positive samples and improve the tumor sensitivity. Third, the pretreatment and post-treatment combined with traditional methods and machine learning methods provide us with the possibility to accurately segment kidney and kidney tumor, and improve the segmentation results. Finally, in the KiTS19 dataset (a total of 210 patients), we divided the training set and test set by 8:2, and then obtained the average dice of 0.96 and 0.77 for the kidney and tumor segmentation, also gained the global dice of 0.96 and 0.92 for kidney and tumor segmentation respectively.
Data Authentication and Anonymization in IoT Scenarios and Future 5G Networks Using Chaotic Digital Watermarking

Future Internet-of-Things (IoT) scenarios and applications are envisioned to be supported by emerging 5G networks. In this context, complex routing schemes for pervasive infrastructures are highly simplified, as every hardware element may stablish its own communication link with a 5G base station. However, this situation also introduces new risks, especially in the security field where innovative cyber-physical attacks and distributed denial of service attacks are becoming more popular and dangerous each day. Thus, data authentication, protection and anonymization in those new applications and schemes is a key challenge to be addressed. Besides, most devices in future IoT systems will be resource constrained, so traditional solutions based on private keys stored in devices' memory and computationally heavy cryptographic algorithms will turn unsecure, inefficient or, directly, impossible to run. Therefore, in this paper we propose a new mechanism to protect, authenticate and anonymize data in IoT systems supported by future 5G networks. The proposed solution employs both digital watermarking techniques and lightweight cryptographic technologies. To generate keys in a secure and simple manner, physical unclonable functions are employed. Besides, to reduce as much as possible the computational cost of algorithms, chaotic dynamics will be considered. In order to evaluate the performance of the proposed solution an experimental validation based on simulation techniques is also carried out.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples

Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/Advanced-Softly-Update-Drop</uri> .
Biased by being there: The persuasive impact of spatial presence on cognitive processing

Immersive media forms such as virtual reality simulations are considered suitable vehicles for persuasive communication because they enhance the perception of spatial presence and non-mediation. Although the persuasive impact of spatial presence has been confirmed multiple times and immersive media have entered the mainstream market, its influence on individuals’ cognitive processing has not yet been analyzed empirically. An experimental study was conducted with a 2 × 2 between-subjects design (N = 129) and varying levels of spatial presence. The quality of persuasive arguments was manipulated to detect the dominant processing mode. In line with the bias hypothesis of the heuristic–systematic model of information processing, the results indicated that individuals who experienced high levels of spatial presence evaluated the content more positively because they used heuristic processing. The positive evaluation consequently led to biased systematic processing, resulting in persuasive effects, even when the arguments were weak. The implications of the results are discussed.
A Motion Planning Algorithm for Redundant Manipulators Using Rapidly Exploring Randomized Trees and Artificial Potential Fields

This paper presents a novel motion planner for redundant robotic manipulators by utilizing rapidly exploring randomized trees and artificial potential fields.Rapidly exploring randomized trees and artificial potential fields are two well-known navigational strategies in the robotics industry; however, their potential benefits and synergy when implemented together has been largely unexplored.In the proposed motion planner, rapidly exploring randomized trees is first used to determine a suitable path for the endeffector to follow that maneuvers around all obstacles in the robot's workspace.Once a path has been determined, attractive and repulsive potential fields are implemented at all points along the path and are used in a gradient optimization algorithm to determine joint trajectories to reach the desired location.To supplement the attractive and repulsive potential fields, an orientation field is proposed to minimize the error between the actual end-effector orientation and the desired orientation during joint trajectory planning.The motion planner is examined through both analytical and experimental evaluation using the 7 degrees of freedom Kinova JACO and Kinova Gen3 robotic arms.The conclusions drawn from this work substantiate the efficacy and superiority of the proposed two-stage motion planner for the control of redundant manipulators in obstacle-ridden environments.
Don't be rude! The effect of content moderation on consumer‐brand forgiveness

Abstract While it is a popular belief that venting helps unload frustrations about negative (customer) experiences, its effects on consumers' emotional states and consumer‐brand forgiveness (CBF) remain to be explored. Given that a lot of customer complaints are made online, brands seem ambivalent about managing these public complaints without violating consumers' right to free speech. In two experiments, we find that writing a customer complaint increases negative emotions regarding an incident. Moreover, brands can mitigate this negative effect by asking consumers to moderate their speech—for example, with the help of a content moderation policy. Specifically, if brands impose restrictions on consumers' freedom to express their frustrations, essentially asking them for self‐censorship, anger levels in their language decrease and CBF increases. We demonstrate that this effect is stronger for consumers with strong self‐brand connection.
Dissipative Stabilization of Squeezing Beyond 3 dB in a Microwave Mode

While a propagating state of light can be generated with arbitrary squeezing by pumping a parametric resonator, the intra-resonator state is limited to 3 dB of squeezing. Here, we implement a reservoir engineering method to surpass this limit using superconducting circuits. Two-tone pumping of a three-wave-mixing element implements an effective coupling to a squeezed bath which stabilizes a squeezed state inside the resonator. Using an ancillary superconducting qubit as a probe allows us to perform a direct Wigner tomography of the intra-resonator state. The raw measurement provides a lower bound on the squeezing at about $6.7 \pm 0.2$ dB below the zero-point level. Further, we show how to correct for resonator evolution during the Wigner tomography and obtain a squeezing as high as $8.2 \pm 0.8$ dB. Moreover, this level of squeezing is achieved with a purity of $-0.4 \pm 0.4$ dB.
An image encryption scheme based on double chaotic cyclic shift and Josephus problem

In recent years, image encryption has become a very popular and effective method to protect digital images. Among many image encryption methods, the image encryption algorithm based on chaotic map has extensive application and profound theoretical basis. In this paper, a novel image encryption algorithm is proposed by combining chaotic map with Josephus problem. The whole encryption process adopts the classical permutation–diffusion structure. In the permutation process, the double chaotic cycle shift algorithm has been proposed by improving the chaotic shift transform method. In the diffusion stage, we extend the definition of Josephus problem by using chaotic map to preserve the Josephus sequence diversity. We then divide the image into sub-blocks and pick one randomly according to Josephus sequence. Furthermore, we change the pixel values of the image through performing modular addition and XOR operations on the pixels between blocks. Through the above process, we get the image encryption algorithm based on double chaotic cycle shift and Josephus problem (JP-DCCS). In the simulation experiment and security analysis, we compare JP-DCCS with the other advanced image encryption algorithms. The results show that JP-DCCS has better performance and higher security.
PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation

Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skeletons and SMPL body model parameters. By casting our learning approach in a student-teacher framework, we avoid using any 3D data such as paired/unpaired 3D data, motion capture sequences, depth images or multi-view images during training. We first train a teacher network that outputs 3D skeletons, using only 2D poses for training. The teacher network distills its knowledge to a student network that predicts 3D pose in SMPL representation. Finally, both the teacher and the student networks are jointly fine-tuned in an end-to-end manner using temporal, self-consistency and adversarial losses, improving the accuracy of each individual network. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach reduces the 3D joint prediction error by 18% compared to previous unsupervised methods. Qualitative results on in-the-wild datasets show that the recovered 3D poses and meshes are natural, realistic, and flow smoothly over consecutive frames.
Toward graphene textiles in wearable eye tracking systems for human–machine interaction

The study of eye movements and the measurement of the resulting biopotential, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human–computer and human–machine interaction (HCI/HMI), and personal medical devices; provided that, seamless sensing of eye activity and processing thereof is achieved by a truly wearable, low-cost, and accessible technology. The present study demonstrates an alternative to the bulky and expensive camera-based eye tracking systems and reports the development of a graphene textile-based personal assistive device for the first time. This self-contained wearable prototype comprises a headband with soft graphene textile electrodes that overcome the limitations of conventional “wet” electrodes, along with miniaturized, portable readout electronics with real-time signal processing capability that can stream data to a remote device over Bluetooth. The potential of graphene textiles in wearable eye tracking and eye-operated remote object interaction is demonstrated by controlling a mouse cursor on screen for typing with a virtual keyboard and enabling navigation of a four-wheeled robot in a maze, all utilizing five different eye motions initiated with a single channel EOG acquisition. Typing speeds of up to six characters per minute without prediction algorithms and guidance of the robot in a maze with four 180° turns were successfully achieved with perfect pattern detection accuracies of 100% and 98%, respectively.
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion

Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion. In this work we present an alternative method for end-to-end scene flow learning by joint estimation of non-rigid residual flow and ego-motion flow for dynamic 3D scenes. We propose to learn the relative rigid transformation from a pair of point clouds followed by an iterative refinement. We then learn the non-rigid flow from transformed inputs with the deducted rigid part of the flow. Furthermore, we extend the supervised framework with self-supervisory signals based on the temporal consistency property of a point cloud sequence. Our solution allows both training in a supervised mode complemented by self-supervisory loss terms as well as training in a fully self-supervised mode. We demonstrate that decomposition of scene flow into non-rigid flow and ego-motion flow along with an introduction of the self-supervisory signals allowed us to outperform the current state-of-the-art supervised methods.
An Adaptive and Robust UKF Approach Based on Gaussian Process Regression-Aided Variational Bayesian

In this study, both adaptive and robust one is proposed, considering that the properties of a classic unscented Kalman filter (UKF) can be degraded severely by the outliers measured in the contamination distribution and the effects of time-varying noise. An adaptive and robust UKF approach on Gaussian process regression- assisted Variational Bayesian is proposed. The Variable Bayesian (VB) method is used to statistically approximate the time-varying noise due to the strong nonlinearity and inaccuracy of the system model. At the same time, Gaussian Process Regression (GPR) has the advantage of machine learning. The observation information trained by the GPR model performs real-time prediction of the sliding window. Based on the above two points, the output source of the measurement information is estimated to realize the robustness of the filter measurement update. When the noise is uncertain and there are outliers in the measurement information, simulation and SINS/GPS integrated navigation of actual tests were performed, respectively. The test outcomes indicate that the algorithm has better nonlinear estimation performance than the traditional method, the effectiveness of the filtering algorithm proposed is verified.