Reviewerly/exHarmony huggingface dataset:
Noise and error analysis and optimization in particle-based kinetic plasma simulations

In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix C(x,y) of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫C(x,y)dy=0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, ρ(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. We repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. We find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. We show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.
Fuzzy Cognitive Maps in Reliability Modeling

The fuzzy cognitive map is considered as a tool for the ranking of factors affecting reliability. The rank of a factor is defined as an equivalent of the Birnbaum importance index in the classical reliability theory. Models and algorithms are proposed for calculation of the importance indexes of single factors and their joint effects on the reliability. The method is exemplified by the reliability and safety of an automobile in the “driver-automobile-road” system subject to the driver's qualification, traffic situation, unit costs of operation, operating conditions, maintenance scheduling, quality of maintenance and repair, quality of automobile design, quality of operational materials and spare parts, as well as storage conditions. The advantages of the method include 1) use of available expert information with no collection and processing statistical data and 2) capability to take into account any quantitative and qualitative factors associated with people, technology, software, quality of service, operating conditions, etc.
A Human Head Detection Method Based on Center Point Estimation for Crowded Scene

The head detector can effectively handle the Angle change of the head caused by motion, and the features required to capture are smaller. Therefore, human head detection is widely used in the practical application scenes of people positioning and counting. The existing head detector uses a large number of anchors which makes the detection efficiency low. And additional post-processing is required, which may result in the loss of real objects in a crowded scene. In this paper, we return to the bounding-box of the head by estimating the center point of the human head without using anchors and post-processing, which improves the detection efficiency. At the same time, we designed a random combination of data augmentation methods and improved the backbone network to improve the accuracy and robustness of the head detector in crowded scenes. Our method achieves excellent speed and precision performance on the SCUT-HEAD dataset, with 0.91 AP and 0.70 EER at 61.5 FPS.
Privacy-Preserving and Scalable Data Access Control Based on Self-sovereign Identity Management in Large-Scale Cloud Storage

Ciphertext-Policy Attribute-based Encryption (CP-ABE) can realize fine-grain access control by data encryption in an untrusted environment and thus has become the promising data security protection mechanism for outsourced cloud storage. Although CP-ABE scheme with single attribute authority (AA) has been extended to multi-AA and threshold multi-AA schemes to deal with single-point bottleneck on both security and performance in large-scale cloud storage, management of identity attributes still depends on a trusted center, which leaves the scalability of user attribute revocation unresolved in large-scale cloud or cross-cloud access. To solve the above problem, the proposed scheme combines blockchain based self-sovereign identity management (BbSSIM) technology and threshold CP-ABE to achieve access control based on the self-sovereign identity, which removes the trusted intermediaries in a decentralized and trustless environment. Besides good scalability, the attribute revocation, key generation and data access process all keep the user anonymous and thus the user’s privacy is well protected.
PAS-Net: Pose-based and Appearance-based Spatiotemporal Networks Fusion for Action Recognition

Human poses play important roles in action analysis. However, most state-of-the-art approaches in action recognition ignore the importance of human poses and rarely leverage the pose information for further improving the recognition performance. In this paper, we propose a novel network architecture, which simultaneously considers the appearance information and pose knowledge for robust action recognition. We explore various architectures for fusing the appearance and pose information rather than simply averaging scores at the final layer. Moreover, a novel training strategy is proposed to reduce the influence of overfitting for limited training data. Extensive experiments show that our method achieves competitive performance on the popular benchmarks, i.e., UCF-101 and HMDB-51.
A Novel Detection Method for Weak Underwater Acoustic Targets Based on RUSBoost

A large number of clutter false alarms cause the difficulty of detecting weak underwater acoustic targets in the active detection process. This paper proposed a new detection method based on RUSBoost. First, analyzing the imbalance of underwater acoustic data. Second, the classification cost ratio of underwater acoustic target echo data and clutter false alarm data was adjusted. The higher classification cost ratio makes the machine learning model more sensitive to the underwater acoustic target echo data in the training process. Finally, a targets-clutter classifier was constructed by combining RUSBoost, which is placed before the detection module. The experimental result shows that RUSBoost can maintain high detection probability and low false alarm probability on receiver performance curve, and its performance is stable under the different number of iterations and the cost ratio. Therefore, RUSBoost can effectively improve the performance of underwater acoustic target detection.
Application of Augmented Reality and Virtual Reality in Education

Augmented reality (AR) and virtual reality (VR) play an important role in today's world. Due to COVID-19, we all are doing many things by online mode. In this virtual place, the importance of AR and VR increases tremendously. With AR and VR tools, students have an inimitable chance to explore historic destinations while remaining in the classroom. Moreover, AR and VR tools provide students the ability to gain deeper understandings of abstract or complex subjects and phenomena that are relatively difficult to understand only by two-dimensional text, pictures, and video. This chapter discusses the similarities and differences of AR and VR and the evaluation of the application of AR and VR into educational programs.
Variational learning of finite shifted scaled Dirichlet mixture models

In this chapter, we propose a variational framework to learn about the finite shifted-scaled Dirichlet mixture model and evaluate its performance as a clustering tool in three medical real-world applications, namely, malaria detection, breast cancer diagnosis and cardiovascular disease detection as well as a text application, namely spam detection. The motivation behind applying variational inference is that, compared to the conventional Bayesian approach, it is much less computationally costly. Moreover, in this method the optimal number of components is estimated along with the parameter approximation simultaneously as part of the Bayesian inference method. Besides, in the variational technique, over-fitting as one of the common issues of deterministic approaches is avoided. To show the merits of our model performance, it is compared with four other widely used methods.
Tor Hidden Services Discovery and Analysis: A Literature Survey

Abstract Hidden services are a feature of Tor(The Onion Router)[1]. It provides anonymity for the service requester while maintaining the anonymity of the service provider. Since it is quite difficult to trace back and locate both parties in the communication, the criminals use hidden services mechanisms to construct various illegal activities in the darknet, which has brought adverse effects to society. In order to prevent the abuse of Tor hidden services, the discovery and analysis of hidden services are particularly important. The aim of this survey paper is to review and compare the literature of the past five years, provide the readers with methods for discovering tor hidden services, along with the various content analysis methods developed and proposed from time to time. we explain their key ideas and show their interrelations.
Geomath: An Augmented Reality Based Geometry Introductory Android-Based Application with Cuboid Tracking

Geometry is the lesson we have learned from the elementary school level, which is divided into two categories: plane geometry and solid geometry. The shapes of geometrical objects can be around us in our everyday life, such as dice with cuboid shape, door with rectangular shape, plates with circular shapes, etc. Even so, the apparatus used for introducing these shapes are many still in the form of books or static media or objects. In this research, innovation is made by creating an application that will show geometry planes and objects using Augmented Reality technology and the 3D Cuboid Tracking method. This method uses a cuboid-shaped marker that has six sides and can be tracked all at once. By using this method, the geometry objects can be seen in 3D, where users can see every object at a different angle and gives better haptic feedback as the cube can be held. In this research, we use ten geometry objects that consist of six solid geometry objects (cube, rectangular prism, triangular prism, sphere, cylinder, cone) and four plane geometry objects (triangle, rectangle, square, circle). This application shows geometry objects with its description and the calculation of surface area, volume, and perimeter and a quite accurate projection with less than 0.5 cm margin error. A test of the application also has been done on Android smartphones and successfully installed on Android 5.1 and higher versions.
Leveraging Deep Learning to Improve Safety of Outpatient Hemodialysis

We are clearly living in the age of big data—and it is only going to get bigger. Health care systems around the world are becoming increasingly reliant on electronic health records (EHRs) and associated information technology to function on a day-to-day basis. Although the use of EHRs has some
Discussion of “Phantom Penis: Extrapolating Neuroscience and Employing Imagination for Trans Male Embodiment”

Straayer synthesizes literary and scientific discourse on the trans phantom penis, presenting a compelling argument for greater roles of diversity and imagination in its conceptualization. In this response I first introduce a recently uncovered historical reference to phantom genitals that precedes those discussed by Straayer and that extends the trans phantom penis beyond the Western canon. I next compare what is known about the sensory and cortical origins of the trans phantom penis with the sparse scientific research on phantom breasts and vaginas. Finally, I elaborate on Straayer's suggestion that sociology be considered in research on trans phantom body parts, illustrating the role of social factors both in phantom body parts and in scientific understandings of sex and gender.
Anomaly Detection of Trackside Equipment based on Semi-Supervised and Multi-Domain Learning

With the rapid development of rail transit, the health status of trackside equipment is more and more crucial to the operation of rail transit. The use of high-precision anomaly detection methods can quickly detect the abnormal conditions of trackside equipment, ensuring the safety of rail transit. In this paper, a new framework of trackside equipment anomaly detection based on semi-supervised and multi-domain deep learning is proposed, which effectively improves the efficiency and accuracy of anomaly detection. Considering the inconspicuous changes in trackside equipment, including screw missing and cable corrosion, it is not feasible to directly use machine learning method for anomaly detection. Therefore, we first match the collected data to get the difference image. Secondly, considering the sparsity of the difference image, the incremental principal component analysis method is used to reduce the dimension of the difference image. Finally, in view of the limited number of training samples and the high cost of manual labeling, we combine semi-supervised deep learning and multi-domain deep learning to effectively train the anomaly detection model. Experimental results show that the proposed method has higher detection accuracy than the traditional artificial anomaly detection and anomaly detection based on shallow image alignment methods.
Virtual Reality and WebRTC implementation for Web educational application development

Real-time collaborative web tools are of great importance in communication between everyone nowadays. WebRTC and similar solutions enable web real-time communication. On the other hand, immersive technologies, like the ones related to Augmented Reality and Virtual Reality, have become particularly valuable for educational platforms. In this paper, WebRTC and VR based web application development has been considered. The application and experimental results focus on the advantages and new possibilities that WebRTC and virtual reality bring. This can be of interest not only for teaching and communication between proffesors and students, but also for education of children with physical disabilities or developmental disorders, like autism.
K-Nearest Neighbor and Naive Bayes Classifier Comparison for Individual Character Classification on Twitter

Twitter is one of the most commonly used social media, especially in Indonesia. People use Twitter social media to express their opinions every day. This allows the DISC method to be applied to find out Twitter user's character. By knowing their characters using the DISC method and Twitter, this can help parties like the HRD without spending more effort in selecting employees. Three main processes are performed in this study, data acquisition, pre-processing, and calculation process. Classification methods, namely Text Naïve Bayes Classifier algorithm and K - Nearest Neighbor, are used to classified and mapping tweet data to the DISC method. As a result, the accuracy of the Naïve Bayes Classifier algorithm is 34.16%, while the K - Nearest Neighbor is 28.33%. So it can be concluded that the Naïve Bayes Classifier algorithm has a higher accuracy of 5.83% compared to K - Nearest Neighbor in classifying a Twitter account with TF-IDF Weighting into DISC method.
Construction of a Complex System Based on Big Data for the Intelligent Service System of Youth Physical Health

The progress of social economy has created a better environment for the healthy development of young people, but the heavy schoolwork and life pressure have caused many students to ignore the scientific management of physical health. At this stage, people need a scientific physical health service system to help students understand their own health data, propose targeted exercise methods and health knowledge, and actively encourage and guide students to participate in physical exercise. The purpose of this article is to cultivate students’ good self-exercise awareness and improve their physical fitness and health. To this end, this article has designed a smart health service system for young people. This article introduces the various service functions in the health management service system and explains in detail the entry, induction, and analysis of student physical health data in the system. The essence of the health intelligent service system is to provide students with targeted healthy exercise strategies through data analysis. This paper studies the health intervention plan of the health intelligent service system. From the experimental data, the improved particle swarm algorithm in this paper increases the effectiveness of the system in adolescent health data mining from 80.5% to 92.19%, which undoubtedly optimizes the system. It helps a lot.
Multi-Unit Market-based Mechanism Design in Cloud Secondary Markets

The on-demand pricing mechanism, which is one of the most common pricing methods for cloud services, implements the pay-as-you-go model for cloud service users. Moreover, it provides the possibility of cost management and the flexibility of the service time for their service users. Since the on-demand mechanism does not provide all the providers' required information, it is not of interest to them. Thus, the service providers are more inclined to use futures contracts so they can accurately estimate the future needs of their users and plan for the requirements. To add the users' required flexibility to futures contracts, a new concept is introduced to cloud ecosystems, called the secondary market. In this secondary market, brokers and reseller buyers act as mediators to provide the required VMs for the service users. This paper provides a mechanism design that includes a market-based pricing model and a resource allocation mechanism in such environments. The proposed mechanism is based on dynamic double auction models and the suggested market price is computed based on the critical point that is obtained from the presence of the agents. Our experimental results prove that the proposed mechanism outperforms the other algorithms in terms of the overall utility, and the allocation efficiency.
Multi-Channel Input Deep Convolutional Neural Network for Mammogram Diagnosis

Medical image diagnosis should consider the information contained in multiple images, not just a single image, such as natural image classification. Mammography is the most basic X-ray screening method for diagnosing breast cancer, and mammograms have four images per patient. Convolutional neural networks should be able to diagnose using these four images. This paper proposes a convolutional neural network to simultaneously concatenate four images to solve the multi-view problem. The proposed network was trained and validated with the digital database for screening mammography (DDSM) and achieved 0.952 area under the ROC curve (AUC) for the two-class problem (positive vs. negative). This paper also proposes a new approach to localize lesions without patch labels or mask labels.
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

We introduce CharacterGAN, a generative model that can be trained on only a few samples (8 – 15) of a given character. Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation. Since we only have very limited training samples, one of the key challenges lies in how to address (dis)occlusions, e.g. when a hand moves behind or in front of a body. To address this, we introduce a novel layering approach which explicitly splits the input keypoints into different layers which are processed independently. These layers represent different parts of the character and provide a strong implicit bias that helps to obtain realistic results even with strong (dis)occlusions. To combine the features of individual layers we use an adaptive scaling approach conditioned on all keypoints. Finally, we introduce a mask connectivity constraint to reduce distortion artifacts that occur with extreme out-of-distribution poses at test time. We show that our approach outperforms recent baselines and creates realistic animations for diverse characters. We also show that our model can handle discrete state changes, for example a profile facing left or right, that the different layers do indeed learn features specific for the respective keypoints in those layers, and that our model scales to larger datasets when more data is available. Code is available at https://github.com/tohinz/CharacterGAN.
An enhanced intelligent classification approach to improve the encryption of big data

Abstract Progression in cloud applications have been hindered due to critical issues such as Data security and privacy. The aggravating concern remains the ability and access of cloud operators in acquiring sensitive data. Consequently, usage and excitement of cloud computing has remained lukewarm, with users remaining skeptical of the level of confidentiality promised. Principally, the financial industry and governmental agencies have shown lackluster and inimical enthusiasm of these indications. The paper serves to analyze the aforementioned issue, and present a creative proposal into cryptography, as a means to virtually restrict cloud service operator’s access to sensitive data. The free-will of cloud operators in handling sensitive data will be undermined. Through this method, file is divided with more accuracy using an intelligent classification technique. Alternatively, a different method can be utilized to find out whether data need splitting to obtain lesser operating time and minimize storage space. The results show that the approach can resolve innumerable risks associated with cloud computing, whilst requiring sufficient computing time using a very good intelligent machine learning classification techniques. As such, novel approach has been proposed which is entitled as a model for Sensitive Encrypted Storage (SES). In this model, three proposed algorithms Convolution Neural Network with Logistic Regression, Elliptic-curve Diffie Hellman-Shifted Adaption Homomorphism Encryption and Decryption have been integrated.