Reviewerly/exHarmony huggingface dataset:
Risk Return Performance of Bitcoin and Alternative Investment Assets in Mixed Asset Portfolios in the Years 2018 to 2020

Research background: Since the financial crisis in 2008, numerous other cryptocurrencies have established themselves in the financial industry alongside Bitcoin. Although the validity of the user cases is still lacking, Bitcoin is already being used extensively in the institutional finance sector, among others. Here, the comparison of Bitcoin to other asset classes in mixed portfolio structures must be taken into account. According to the latter, far-reaching areas of investigation emerge by adding Bitcoin in the evaluation of risk-return ratios of mixed portfolio weightings. Purpose of the article: The objective of this paper is to examine, within the framework of Harry Markowitz’s efficiency theory, the impact of including Bitcoin as an investment asset for the risk-return ratios of mixed portfolio structures. Methods: The statistical analysis is based, among other things, on paired sample tests, where the return and volatility values are tested for significant differences in the selected test values. Findings &amp; Value added: The statistical investigations show that the introduction of Bitcoin leads to advantageous return structures, but at the same time to significantly increased volatility values of the examined portfolio constellations. Setting a regional focus of the investment assets in the investigations led to a simplified evaluation basis and at the same time offers the scientific space for further investigations.
3D Real-time Face Acupoints Recognition System Based on HoloLens 2

Acupoints positioning is a significant part of Traditional Chinese Medicine (TCM) massage. However, the number of acupoints are large and their functions are complex, trainees usually need the guidance of doctors to accurately locate them. In order to improve the efficiency of acupoints location, this paper proposes a three-dimensional (3D) real-time face acupoints recognition system based on HoloLens 2. Through wearing the mixed reality device HoloLens 2, users can directly see the acupoints stacked on the human face. In this paper, we use a new face tracking technology, combined with landmark detection and acupoints positioning method in Acupoint Science to detect and track 3D facial acupoints in real time, and realize visualization on HoloLens 2. The positioning error of our system is about 2.55 mm and the frame rate is 20.59 FPS. The experimental results show that our system has good real-time and accuracy, can realize the automatic positioning of acupoints, and is conducive to the teaching and practice of TCM massage theory.
Incremental Forensics Snapshot of Digital Evidence Method Using Differencing Algorithm and Blockchain

The application of the E-government has been improving the quality of public service in many countries. The emergence of blockchain technology with the characteristics of tamper-proof, traceability and decentralization has brought new opportunities for E-government development. As one of the most suitable technical solutions for E-government, consortium blockchain has the advantages of better controllability, faster transaction speed, and access control. However, considering consortium blockchain’s partial decentralization, there is a possibility of collusion among multiple participants. Therefore it is necessary to find effective regulatory methods to enhance the credibility of consortium blockchain. This paper presents a blockchain snapshot forensics method based on blockchain and the differencing algorithm. By extracting incremental information of digital forensics content through the differencing algorithm, we can significantly improve the storage performance of the supervision blockchain. Our method can generate multiple versions of forensics data for each original data in the business blockchain and check out the specified version of digital forensics according to every forensics data. Compared with the full data storage scheme, our scheme can reduce the storage cost of blockchain by about seven times on average. Furthermore, compared with the summary information storage scheme, our scheme can recover complete evidence data. And the time consuming of incremental data extraction and recovery are within the acceptable range for the supervision blockchain.
A Color Image Encryption Using One Quaternion-Valued Neural Network

Color image encryption via real-valued chaos system generally requires multiple real-value systems. To this end, this article presents a encryption algorithm for color image that requires only a quaternion-valued neural network system. First, an inertial memristor-based quaternion-valued neural network (IMQVNN) is proposed and the exponential synchronization criteria for the addressed systems are given. Then, an example and its simulation results are given to verify the rationality of the synchronization criteria. Based on the drive-response systems, a encryption algorithm for color image is therefore put forward. Finally, the effectiveness of the proposed cryptosystem is verified by the simulation results and security performance analysis.
Novel GAN Inversion Model with Latent Space Constraints for Face Reconstruction

This paper considers how to encode a target face image into its StyleGAN latent space accurately and efficiently, with applications to allow the various image editing method being used on the real images. Compared with optimization-based methods using gradient descent on latent code iteratively, the learning-based method we adopt can encode target images with one forward propagation, which is better suited for real-world application. The key advances in this paper are: adopting the face recognition model as a constraint to keep the identity information intact and adding a classifier to encourage latent code to retain more attributes possessed in the original image. Experiments show our method can achieve an excellent reconstruction effect. The ablation study indicates the proposed design advances the GAN Inversion task qualitatively and quantitatively. However, the method may fail when there are other objects around the target face and generate a blurry patch around that object.
Efficient, Low-Cost, Real-Time Video Super-Resolution Network

Video Super-Resolution (VSR) task aims to reconstruct missing high-frequency information lost in degradation. Researchers have proposed many excellent models. However, these models require large memory and high computational cost. In this paper, we propose a novel VSR model called StudentVSR (StuVSR) which is a unidirectional recurrent network. To guarantee StuVSR can generate sufficient high-frequency information, we propose Inceptual Attention (IA) mechanism. Meanwhile, to compress the model size, we utilize the idea of knowledge distillation. We take an auto-encoder network as teacher and redesign the knowledge distillation mode. StuVSR employs extremely small parameters and accomplishes the VSR task in a rapid manner. StuVSR can generate 30-frame-per-second (FPS) 1080p-2k videos in real-time. We conduct comparison experiments to prove the superiority of StuVSR and StuVSR achieves the highest Peak Signal to Noise Ratio (PSNR) score among 16 state of the arts. We also explore the function of the inceptual attention and the knowledge distillation mode through ablation experiments. We will publish the codes at https://github.com/Dawn3474/StuVSR.
Data Security: Vital Demand in Cloud Computing

Cloud Computing is a popular buzzword currently in the market, the essential concerns for both Cloud providers and consumers are Confidentiality, Integrity, Availability, Authenticity, and Privacy . Security in Cloud computing is a crucial important and critical aspect, and involves numerous issues and problem associated to it. Cloud service provider and the cloud service consumer must and should make sure that the cloud is stay protected from all the external threats and risks so that the customer does not get any problem such as loss of data or data theft. There is also an instance where a malicious user can entered in to the cloud by acting like a legitimate user, in consequence infecting the entire cloud and that effects many customers who are sharing the affected cloud. In this we will discuss how to administer security for the data from the unauthorized users and accommodating integrity to the users.
Technology Increasing Capacity Protected Channel Delivery Video Data Telecommunication Systems Critical Infrastructure

The technology of video stream closure has been developed, which is based on the detection and closure of significant structural units. This technology employs the introduction of the closure marker into the code structure of the video stream for use on unmanned aerial vehicles. This makes it possible to meet departmental requirements for the speed of transmission in conditions of increased frame rates. At the same time, the quality of the updated video data is higher than when using other concealment methods. This is since the encoding process takes into account the processing of the base video frame substantial structural units.
An Efficient System to Predict Customers’ Satisfaction on Touristic Services Using ML and DL Approaches

In the last decade, neural networks NNs become a favorable solution for many applications in artificial intelligence AI. For instance, the majority of tourism companies have professional websites where customers can book: flights, bus and taxi trips, hotels, restaurants, etc. they can also compare services in terms of prices, locations, services quality, and other interesting criterion. For this purpose, the used dataset consists of a sample of hotel reviews provided by customers who have reserved recently. Analyzing these reviews will help companies to know if their services are suitable for customers, satisfy their needs and what is the degree of this satisfaction. i.e., customers are happy or not? Satisfied or not? Our main objective in this work is to develop an efficient and intelligent system based on NNs which allows us to predict how customers feel about the provided services. To accomplish this work, we have proceeded to the classification task using many machine learning algorithms, including LDA, KNN, CART, NB, and SVM. Then, we proposed in the second stage a deep neural network DNN model to perform the same task. Finally, we established a short comparison between the different algorithms. In the programming stage, we benefited from the large opportunities offered by Python language, as well as Tensorflow and Keras libraries.
Cascaded Dimension Reduction for Effective Anomaly Detection

Recent years have witnessed the growth of the size and dimensionality of data from various applications at an unprecedented rate. Detecting anomalies in these high-dimensional data has a great significance yet remains challenging due to the sparsity, noise, and irrelevant features in the high-dimensional data. Principle Component Analysis (PCA) and AutoEncoder (AE) are the two most widely used dimension reduction (DR) methods where it reduces the number of features while capturing the most necessary information of the original data. PCA alone is, however, less effective for complex data though it is fast and has explained variance to measure the preserved information of reduced data. While combining PCA with AE can be more effective, determining optimal latent representation is a challenging problem. In this work, we propose a cascaded dimension reduction method (CDR) for effective anomaly detection. In CDR, we combine AE and PCA to reduce dimension size significantly while incorporating a knee point detection algorithm to automatically select optimal dimension size k that maximizes the anomaly detection accuracy. Our extensive evaluation of various datasets and anomaly detection models demonstrate that our proposed CDR can significantly reduce dimension size while preserving the most necessary information for effective anomaly detection. As a result, CDR achieves 80%~98% of reduction ratios and 4~21x of speedup and outperforms state-of-the-art anomaly detection methods.
Exploration and Visualization of Municipal Worcester Data

The Department of Public Works and the Department of Inspectional Services in Worcester, Massachusetts receive and respond to service ticket requests submitted by citizens regarding issues from waste and vandalism to imploding roofs. There is often a lack of understanding between the local government and its citizens about the completion process of these requests which can result in confusion and tension. To better bridge the gap between citizens and the government regarding these service ticket requests, we explored the use of interactive visualizations in a web application. Additionally, we explored the design space of a ticket tracking system, creating one that makes ticket information readily available to residents. These applications were developed to provide a better understanding of the ticket-completion process, resulting in fewer complaints on the same issue and less stress in ticket completion that can be extended to other cities.
Construction of Remote Sensing User Growth System Based on Hooked Mode

Remote sensing information has been widely used in various industries of national economy. In order to promote the benign ecological development of remote sensing information producers and users, it is necessary to build a growth system of remote sensing users. This paper analyzes the common methods of typical Internet user growth system, and summarizes the essence of user growth system. Aiming at the construction of remote sensing user growth system, firstly, the target users, application scenarios, product concepts, service platforms and strategic directions of remote sensing information are defined. Then, the driving force based on hooked model is designed, and the RFA model suitable for the growth of remote sensing users is constructed. Finally, a closed-loop remote sensing user growth system is constructed by taking the integral method as an example, so that users can continuously apply remote sensing information, Promote the continuous development of remote sensing means and form a virtuous circle.
Machine reading between the lines (RBL) of medical complaints

In this chapter, we formulate the problem of Reading Between the Lines (RBL) as forming natural language (NL) expressions that are not included by an author in text but that are believed to be assumed by the author. RBL is necessary to identify hidden meaning of text, something the author intends to conceal, not to confess or explicitly state. In medical complaints, RBL is essential to reveal the information the patient does not want to share with the physician. We rely on discourse analysis to identify a position in text where RBL is expected to yield results, syntactic generalization to formulate a query for web mining, and information extraction from documents to obtain RBL results. The RBL procedure is evaluated on various texts in the health domain with respect to relevance of RBL expressions to what we believe the author intended to say. The input to the RBL evaluation procedure is a text with a sentence or phrase removed. The output of the RBL is subject to similarity assessment with the removed sentence and if such similarity is high, we conclude that RBL succeeds. We also evaluate how RBL improves a search recall by extending a search index with RBL expressions. The conclusion is that the proposed RBL procedure is an efficient way to obtain and represent invisible knowledge in text.
CTAP: Covid-19 Tracking Application

In this paper, we propose an android mobile application, Covid-19 tracking application, that checks the social distance between the users, informs them of any encounter with a user positively tested with the virus, and informs the user of the basic coronavirus stats and information. This proposed application has been successfully implemented using Kotlin for the front-end, and Java for the back-end. The implementation of this application resulted in alerting the users who are violating social distancing by standing less than 2 meters next to other users, and notifying them of any encounter with a user positively tested with Covid-19. The percentage of error in distance measurement using Global Positioning System (GPS) is low, having an average value of 3.304%.
Open-Set Recognition with Dual Probability Learning

The open-set recognition task is proposed to handle unknown classes that do not belong to any of the classes in training set. The methods should reject unknown samples while maintaining high classification accuracy on the known classes. Previous methods are divided into two stages, including open-set identification and closed-set classification. These methods usually reject unknown samples according to the previous analysis of the known classes. However, this would inevitably cause risks if the discriminative representation from the unknown classes is insufficient. In contrast to the previous methods, we propose a new method which uses a dual probability distribution to represent the unknowns. From the dual distribution, the boundary of known space is naturally derived, thereby helping identify the unknowns without staging or thresholding. Following this formulation, this paper proposed a new method called Dual Probability Learning Model (DPLM). The model built a neural Gaussian Mixed Model for probability estimation. To learn this model, we also added the normalized joint probability of latent representations into the objective function in the training stage. The results showed that the proposed method is highly effective.
FORMATION OF THE SKILLS OF EDUCATION SELF-ORGANIZATION OF PRE-SERVICE TEACHERS OF CHOREOGRAPHIC COURSE OF STUDY: PEDAGOGICAL PRINCIPLES AND CONDITIONS

The article deals with the issue of improving the preparation of pre-service teachers of choreographic course of study for the independent arrangement of their educational activities in the process of professional education. The author defines that the ability to self-organization is one of the key competencies of pre-service teachers, which is based on the skills of educational self-organization. With these skills, students gain the opportunity to effectively plan, diagnose, monitor, adjust and evaluate their learning and its effectiveness. The paper substantiates the essence of the concept of «skills of educational self-organization» and discloses its content, taking into account the specifics of special disciplines and the nature of educational (scientific-theoretical, methodological, creative-interpretive, pedagogical, ballet-master, artistic and performing) activities of pre-service teachers-choreographers. Focusing on the current trends in the increase in the number of academic hours planned for independent work, and the gradual transition in the training of specialists to distance and mixed forms of education, the article emphasizes the importance of developing and applying special pedagogical principles and conditions for organizing vocational education of students-choreographers based on educational self-organization and self-education. It has been determined that scientific principles underlie the study of pedagogical theories, processes and phenomena and guide the strategy of their development. In the context of the formation of the skills of educational self-organization of pre-service teacher of choreographic course of study, expedient pedagogical principles are determined: the principle of personal-semantic coordination, the principle of professional self-presentation, the principle of personalization of training. It is indicated that the developed special pedagogical conditions can create favorable circumstances in the educational process for achieving the set goal. Effective pedagogical conditions for the successful formation of the skills of educational self-organization of pre-service teacher of choreographic course of study are: activation of students’ motivation for independent activity, support for the initiative of self-education of future specialists, ensuring the development of the volitional sphere of students.
Research Progress Analysis of User Interest Model of Recommender System based on Knowledge Graph

The user interest model is the premise of personalized recommendation. To grasp the research hotspots and explore new research directions in them for recommender systems. This paper analyses the relevant core documents in the China National Knowledge Infrastructure (CNKI) database from 2010 to 2020 based on the knowledge graph. The author's co-occurrence map, high-frequency keyword co-occurrence map, and cluster map were drawn. The research findings are as follows: (1) At present, the cooperation between researchers is not strong enough, and the core research team has not been formed. (2) Research hotspots mainly focus on data acquisition and modeling, recommendation algorithms and techniques, and updating and application of user interest models. (3) Future research should strengthen multi-dimensional data fusion and improve the accuracy of models, enrich and improve the interest model update mechanism, enhancing research on user-based interest model personalization services for recommender systems.
Old Wine in A New Bottle: A Homogeneous Fraud Sites Discovery Framework

Internet fraud is a serious threat to the normal order of the Internet, endangering the personal and property safety of its users. Nowadays in China, the most rampant forms of Internet fraud include false winning, false part-time job review, false investment, and phishing websites. Fraud gangs rely on overseas infrastructure and multiple domain names to evade domestic investigation. Meanwhile, they use common open source templates to build such apps or websites to save cost, which enables fast fabrication of apps/websites for various fraud scenarios with limited modifications. In this way, those low cost apps/websites are highly homogeneous in terms of page elements, in other words, "Old Wine in New Bottles". This paper presents a framework that combines both passive and active means for detection of such homogeneous apps/websites from raw Internet traffic. It is a synthesis of rapid detection, convergent verification, and feedback iteration. Having been deployed and verified on an ISP-level backbone in China, the framework is proved to be featured with high detection efficiency, low false-positive rate, and agility of updates.
A Method of Detecting Erythrocytes and Spirochaete in Darkfield Microscopy using Attention Unet

Blood circulates throughout the whole body to provide nutrition and deliver waste products to the excretory organs, so it contains various information about living things. Recently, by analyzing microorganisms such as bacteria and viruses in the blood, it is possible to simply diagnosis whether or not a patient has cancer. Therefore, there is a need for a method that can automatically detect erythrocytes and bacteria in the blood, and it has great significance in computer vision and the medical field. Therefore, in this paper, we propose a method for detecting erythrocytes and spirochaete(one of the bacterium) in blood from darkfield microscopy obtained from Kaggle. The proposed method uses Attention Unet, which improves performance by adding an attention gate to Unet, one of the well-known deep learning models. To evaluate the proposed method, we use Unet and Attention Unet to detect erythrocytes and spirochaete and create a confusion matrix to compare accuracy, precision, sensitivity, and specificity. As a result of the experiment, it was confirmed that Attention Unet showed better detection performance than the Unet.