Reviewerly/exHarmony huggingface dataset:
A new deep fake method based on background removal

The modern digital space is saturated with a huge amount of data in the form of images and videos every day. All information contained is important to users, organizations and other consumers. It should be noted that the speed of information dissemination is so high that sometimes, in order to trace its original source, it is necessary to spend large computing resources. Moreover, during the search for the original source, information can be distorted beyond recognition and acquire new properties in the form of details and additional links. Attackers change information, as a rule, in order to compromise an individual, incite conflicts, undermine the reputation of individuals and legal entities, etc. Among the most modern and popular means is DeepFake technology, with the help of which a person&#x0027;s face can be replaced on an image or video. The paper proposes a new method for generating distorted images, based on removing the background in the image, to improve the visual quality of distortion.
Three Approaches to Risk Management in the Cloud

Since companies have increasingly used cloud services for their businesses, security risks are important issues for their business success. The paper presents the understanding of cloud computing and risk management in the cloud. For managing cloud risks, three risk management approaches are introduced.. This paper will give some inferences that companies choose the best cloud network to enhance their businesses and use the appropriate risk management approach to mitigate their risks within the cloud environment.
Establishing The Photovoltaic Output Model With PIC-LS-SVM Based on Curve Characteristic Index Dimensionality Reduction

In order to cope with the challenges brought by large-scale photovoltaic grid connection to grid dispatching, a PIC-LS-SVM model is proposed to calculate photovoltaic output. Firstly, this paper select the characteristic indicators of the photovoltaic power generation output curve, and use the PIC clustering algorithm to cluster the original data into different training sets. Secondly, it perform correlation and principal component analysis on the meteorological data in each training set, extract comprehensive meteorological factors, and determine the category of the test data according to the characteristics of the comprehensive meteorological factors. Finally, the LS-SVM algorithm is used to calculate the PV output value of the test data. The PIC-LS-SVM algorithm can obtain good results in the calculation of photovoltaic output value, and the application of the algorithm to the establishment of photovoltaic output model is feasible.
Lightweight Monocular Depth Estimation Based On Perceptual Loss and Network Slimming

The performance of deep learning-based depth estimation depends on encoding layers, decoding layers, and loss function. In this paper, we propose a lightweight monocular depth estimation method by considering those three elements. First, we introduce perceptual loss to represent the characteristic of the depth information and the proposed loss improves accuracy. Second, encoding and decoding layers are analyzed and redesigned for lightweight depth estimation. Finally, encoding layers are pruned for more efficient architecture. In the experiment, the proposed method is evaluated on the NYU Depth V2 dataset and KITTI dataset and it shows that proposed loss and network slimming improve the accuracy of depth estimation with less memory and computational complexity. The proposed model is also deployed to the mobile device, iPhone 11 Pro. It runs more this 30 fps and the inference time is 5 ms.
Adaptive replication strategy based on popular content in cloud computing

The cloud is an infrastructure that provides decentralized on-demand services. It allows consumers to pay only for the services they use. The consumer is the important entity in the cloud. The violation of the SLA contract between the consumer and the provider often leads to consequences because the service provider has to pay penalties. Data replication is emerging as an ideal solution to meet the new challenges of the cloud. This paper proposes a new replication strategy based on the popularity of data. This strategy adaptively selects the files to be replicated to improve the overall availability of data in the system, minimize query response time, and achieve the required quality of service. In addition, it dynamically determines the number of replicas to add and the best locations to store them. Experimental results show the effectiveness of the proposed strategy.
Prevention of the psychoemotional burnout of academics during their professional activities

The article shows that psychoemotional burnout is one of the most common syndromes, developing against the backdrop of continuous exposure to stressful situations, and it leads to intellectual, mental, and physical fatigue and exhaustion. The pedagogical activities of academics lead to burnout syndrome among most professionals of this occupation. The aim of the article is theoretical substantiation of the concept of burnout syndrome, experimental study of its symptoms characteristic of academics, and presentation of recommendations for its prevention. The study involved 59 academics of different ages and records of work. The analysis of literature sources allows stating that the study of the burnout syndrome among academics requires a further search for solutions. The academic profession is one of those for which burnout syndrome is the most common because the professional activity of academics is recognized as one of the most emotionally stressful. It was found that burnout is exhaustion, which manifests itself in emotional devastation and fatigue, and depersonalization, in particular, depersonalization of relationships with people. It is established that most teachers are characterized by inadequate emotional response, reduced interaction with colleagues, termination of professional responsibilities, the desire to stay alone, intensified emotional thriftiness. A pronounced feature for teachers is the reduction of personal achievements when they negatively assess themselves, their professional achievements and success, express professional integrity in a negative way, and limit their opportunities and responsibilities.
Characterising users’ task completion process in learning-related tasks:A search pace model

This study investigated users’ searching, reading and writing interactions and their activity transitions during task completion process when users were collecting information for learning-related search tasks. Task completion process was defined as the process users started to search till the time when they have collected enough information to accomplish the search tasks. The data analysis was conducted from a new process perspective through synthesising macro- and micro-process levels. Four evenly distributed stages were divided according to the total task completion time in each search session. Our results demonstrated that users generally experienced three sub-processes during task completion process: exploration, accumulation and composition/reporting. Exploration sub-process is basically the first quarter of the total task completion time, during which users often issue more queries and visit more search engine result pages (SERPs) to collect information, and the dominant activity transition is switching between searching and reading; accumulation sub-process is mainly the second and third quarters of the total task completion time, during which they visit more unique content pages, have more revisits per content page, and they switch between reading and writing activities frequently; the last stage is composition/reporting sub-process, which is dominated by writing, and users often switch between writing and reading, and between writing and searching. Based on these findings, we propose a search pace model to describe how users proceed from the beginning to the end of task completion process in these three sub-processes. The methodology applied has been proved to be effective to examine users’ interaction behaviours from the process perspective on both the micro- and macro-levels. The findings of this article help us understand how users proceed their dynamic searching, reading and writing behaviours for learning-related tasks, and also have implications for the design of search systems that support learning-related tasks.
The ore prospecting prediction model for the Huili copper orefield in Sichuan Province, China

The prediction theory and methodology of ore prospecting were developed from an in-depth study of 129 typical deposits in China. It has been verified to be an effective method that is particularly suitable for the initial ore prospecting. In this method, the internal and external factors of metallogenesis are combined together to construct a geological model of prospecting prediction, which consists of metallogenic geological body, metallogenic structure, metallogenic structural plane and metallogenic characteristics. The Huili area is located in the western margin of the Yangtze Plate, where the regional metallogenic geological conditions are superior, and a series of unique iron-copper deposits were formed. In recent years, great breakthroughs and progress have been made in the deep and peripheral areas of the Huili copper orefield. Herein, we take the Huili copper orefield as a typical example to illustrate the specific application of this method in deep ore prospecting of hydrothermal deposits. The metallogenic geological body is the ore-hosting volcanic rocks (albitite in the Hekou Group), and the main metallogenic structure and structural planes are interfaces between basic (intermediate) volcanic rocks and sedimentary rocks and the possible volcanic vent. Combined with the summary of metallogenic characteristics, we constructed a geological model for ore prospecting in the Huili copper orefield.
All Characteristics Preservation: Single Image Dehazing based on Hierarchical Detail Reconstruction Wavelet Decomposition Network

Single image haze removal is crucial in computer vision. In open literatures, two kinds of dehazing strategies (prior-based and learning-based methods) have been developed. However, they have a trade-off between detail preservation and the image quality. Prior-based methods reconstruct the detail well but have lower image quality while learning-based methods achieve better recovered quality but lose the detail. In this paper, to mitigate this dilemma, a hierarchical architecture using the discrete wavelet transform (DWT) is proposed. It divides the dehazing problem into two parts: detail and background reconstruction. Based on investigating how haze affects the image in the wavelet domain, two networks for detail and background reconstruction are proposed. To avoid color distortion and the detail loss, the anti-vanish wavelet loss and the bound penalty are proposed. The multi-level wavelet component discriminator is proposed for further improvement. Experiments show that the proposed network can achieve superior performance in all metrics.
Multisensor Selection for Multitarget Tracking Based on Information Divergence

This paper presents a novel information divergence based multisensor selection approach for multitarget tracking. Multitarget states are modelled as multi-Bernoulli random finite sets and multisensor selection is studied under the partially observed Markov decision process (POMDP) framework. Multisensor selection under POMDP is essentially a global combinatorial optimization problem that is challenging when the number of sensors is large. To avoid the complicated combinational optimization, the selection of multisensor is divided into sub-selection of each individual sensor. Informative sensors are selected sequentially from the candidates, and the Rényi divergence between prior and posterior multi-Bernoulli density is used as the selection criterion. Simulation results validate the effectiveness and efficiency of the proposed approach.
Contrast Restoration of Hazy Image in HSV Space

In this paper, a new haze restoration algorithm is proposed to recover hazy images. Hazy images always suffer from color distortion, detail-information loss and contrast reduction, which degrade the visual quality of the image seriously. To address the issue, this paper introduces an effective and robust hazy image enhancement method without any dedicated hardware or prior knowledge. Firstly, the scene depth can be estimated based on the dark channel prior. Additionally, the saturation and brightness are adaptively corrected according to the scene depth. Finally, the gradient algorithm is used to obtain a better restored result. A large number of experiments are conducted, which demonstrates the superior performance of the proposed method compared with other state-of-the-art techniques in terms of both subjective and objective evaluations.
Multi-paradigm Approaches to Training in Mechanical Engineering: In Diagrams and Commentary

This article is devoted to the analysis of electronic resources of mechanical engineering — engineering education and training of engineering personnel in the era of INDUSTRIALIZATION 4.0. The paper is based on the statistics of the Science and Education Electronic Resources Pool (SEERF) from 1998 up to present. The information on developments in engineering education and their statistical processing is automated by collector_stat program developed by personnel of the JERLF. The electronic resources of education in the field of mechanical engineering are presented by technical universities of the country. Thus, the object of the research is technical universities. The subject of the study is electronic resources of education in the field of mechanical engineering. The methods of research are methods of statistics, content analysis, logic.
A Statistical Language Model for Pre-Trained Sequence Labeling: A Case Study on Vietnamese

By defining the computable word segmentation unit and studying its probability characteristics, we establish an unsupervised statistical language model (SLM) for a new pre-trained sequence labeling framework in this article. The proposed SLM is an optimization model, and its objective is to maximize the total binding force of all candidate word segmentation units in sentences under the condition of no annotated datasets and vocabularies. To solve SLM, we design a recursive divide-and-conquer dynamic programming algorithm. By integrating SLM with the popular sequence labeling models, Vietnamese word segmentation, part-of-speech tagging and named entity recognition experiments are performed. The experimental results show that our SLM can effectively promote the performance of sequence labeling tasks. Just using less than 10% of training data and without using a dictionary, the performance of our sequence labeling framework is better than the state-of-the-art Vietnamese word segmentation toolkit VnCoreNLP on the cross-dataset test. SLM has no hyper-parameter to be tuned, and it is completely unsupervised and applicable to any other analytic language. Thus, it has good domain adaptability.
ANN and PSO based Approach for Solar Energy Forecasting: A Step Towards Sustainable Power Generation

For proper designing and development of solar photovoltaic system, forecasting of solar energy becomes very important. Power developed by the solar photovoltaic system depends upon meteorological parameters like temperature solar irradiance. Variation in these parameters causes variation in power generated by the photovoltaic system. Hence forecasting becomes important. In this paper solar energy forecasting is done using artificial neural network and particle swarm optimization based artificial neural network. Artificial neural network is well established method used for forecasting purpose. However there output can further be improved by applying optimization technique. Here particle swarm optimization technique is used MATLAB software is used for coding the optimized neural network and ‘nftool’ application is used for simple artificial neural network. For ANN percentage error comes out to be 4.18% and for ANN-PSO it comes out to be 3.23%.
Automatic verification method of progressive image model matching information based on machine learning

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.
Token Level Evaluation and Feature Enhancer for Transformer-based Models

Most recent state-of-the-art models in the natural language processing (NLP) field such as BERT, ALBERT and XLNet share a common architecture of including an embedding and Transformer encoder and/or decoder layers (aka. Transformer blocks). Here we propose Token Level Evaluation and Feature Enhancer (TLEFE) to be added on any of the Transformer-based models. The TLEFE is supposed to upscale token features for every token in the sentence passed into the system. Through experiments using datasets provided by SemEval-2020 Commonsense Validation and Explanation, we have demonstrated that TLEFE applied model performs better than plain model with negligible additional number of parameters.
QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.
Introduction

In modern computation systems based on the new principles of a quantum mechanics, all standardized on national and/or international level cryptographic algorithms are the verification subject of their cryptographic stability in different applications, as well as an assured safety level of information systems, technologies, and communication protocols etc. Rising of new computing systems (including quantum computers) along together with available and usable theoretical advances of quantum cryptoanalysis creates some dangerous premisses, when cryptographic protection methods applied can not ensure the required stability level. Therefore, the analysis, research, developing and promotion of various cryptographic devices for protecting information must be performed with consideration of recent trends and achievements of cryptographic science, in particular possibilities of the quantum cryptographic ciphers, ciphersystems, algorithms and protocols of cryptomodification analysis in various practical applications.
Fool Me Once: A Study of Password Selection Evolution over the Past Decade

Passwords have been around for many decades and have tenaciously remained the primary means of identification and authentication. Assuming that the communication channel is not intercepted, the strength of security provided by passwords is largely dependent on two factors: password selection and password storage mechanism. While both areas have been looked into by researchers in the past, there is no consensus to suggest whether or not humanity has moved towards choosing stronger passwords, notwithstanding strong password enforcement policies. One of the key reasons behind this shortcoming is the lack of data about individual credentials in leaked datasets, which usually contain only usernames and passwords. To the best of our knowledge, we are the first researchers to enrich the attribute set of any user credential database, thus allowing deeper insights. We outline the method we devised for adding new attributes (time-stamp and source inference) to a dataset of 1.4 billion user credentials. Subsequently, we use our modified dataset to determine how passwords have evolved overtime with respect to strength and whether humankind as a whole has learned from its past mistakes.
Edge Enhancement Loss Function for Target Object IR image Super Resolution

In this research, we propose a loss function for WDSR (Wide Activation Super Resolution) model to enhance the edge of target object. In the design of our proposed loss function, in addition to the basic pixel error, we take other criteria including edges, features and residuals in edges regions into account. Edges are the fundamental structures of the image. Feature map is the element that capture the main characteristics of the images. And the residuals on edges can affect the visualization of the image. With the combination of the three items and basic pixel loss, in the end of our experiment, we manage to enhance the edge portion of the images.
RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL

The emerging of shuffle model has attracted considerable attention of scientists owing to his unique properties in solving the privacy problems in federated learning, specifically the trade off problem between privacy and utility in central and local model. Where, the central model relies on a trusted server which collects users’ raw data and then perturbs it. While in the local model all users perturb their data locally then they send their perturbed data to server. Both models have pron and con. The server in central model enjoys with high accuracy but the users suffer from insufficient privacy in contrast, the local model which provides sufficient privacy at users’ side but the server suffers from limited accuracy. Shuffle model has advanced property of hide position of input messages by perturbing it with perturbation π. Therefore, the scientists considered on adding shuffle model between users and servers to make the server untrusted where the users communicate with the server through the shuffle and boosting the privacy by adding perturbation π for users’ messages without increasing the noise level. Consequently, the usage of modified technique differential privacy federated learning with shuffle model will explores the gap between privacy and accuracy in both models. So this new model attracted many researchers in recent work. In this review, we initiate the analytic learning of a shuffled model for distributed differentially private mechanisms. We focused on the role of shuffle model for solving the problem between privacy and accuracy by summarizing the recent researches about shuffle model and its practical results. Furthermore, we present two types of shuffle, single shuffle and m shuffles with the statistical analysis for each one in boosting the privacy amplification of users with the same level of accuracy by reasoning the practical results of recent papers.