Reviewerly/exHarmony huggingface dataset:
Feature enhancement composition model for heavy rain removal on single image

The current single image rain removal method is not ideal in the task of rain and haze removal. In this paper, based on the physical imaging mechanism under heavy rain conditions, a feature enhanced composition model, FEC-Net, is constructed by combining physical model-driven and data-driven methods. The feature maps, which contain rain streaks, atmospheric light, and transmission map information were obtained by three convolution layers. These feature maps are enhanced by the feature enhancement module. Finally, a clear image is obtained according to the formula of the atmospheric scattering model. The selective enhanced processing of key output data greatly improves the processing effect of the network. Extensive experiments show that our FEC-Net achieves better accuracy and visual improvements against state-of-the-art methods.
CoinChain: Effective Coin Mixing Tool for Anonymous CryptoCurrency

Many anonymous cryptocurrencies have been introduced to address the privacy issue in Bitcoin. Compared to traditional security schemes like hashing and encryption, the security measurement for cryptocurrency is less precise. In hashing, there is a direct security challenge, such as reproducing a hash collision. However, in anonymous cryptocurrency, a direct security challenge is not precise. Many anonymous cryptocurrency proposals address this issue in the form of cryptographic proof. This paper describes a brief evolution of anonymous cryptocurrency, followed by a brief description of CoinChain Protocol. This paper also proves that CoinChain has a high anonymity set. A methodology and code to produce a simulated blockchain from a list of actual transactions for any third party to decipher is also described. If a third party can correctly decipher the simulated blockchain into actual transaction, then the privacy aspect of the cryptocurrency is consider broken.
Predicting the Execution Time of the Primal and Dual Simplex Algorithms Using Artificial Neural Networks

Selection of the most efficient algorithm for a given set of linear programming problems has been a significant and, at the same time, challenging process for linear programming solvers. The most widely used linear programming algorithms are the primal simplex algorithm, the dual simplex algorithm, and the interior point method. Interested in algorithm selection processes in modern mathematical solvers, we had previously worked on using artificial neural networks to formulate and propose a regression model for the prediction of the execution time of the interior point method on a set of benchmark linear programming problems. Extending our previous work, we are now examining a prediction model using artificial neural networks for the performance of CPLEX’s primal and dual simplex algorithms. Our study shows that, for the examined set of benchmark linear programming problems, a regression model that can accurately predict the execution time of these algorithms could not be formed. Therefore, we are proceeding further with our analysis, treating the problem as a classification one. Instead of attempting to predict exact values for the execution time of primal and dual simplex algorithms, our models estimate classes, expressed as time ranges, under which the execution time of each algorithm is expected to fall. Experimental results show a good performance of the classification models for both primal and dual methods, with the relevant accuracy score reaching 0.83 and 0.84, respectively.
Data Analysis System of Chinese Business English From SQL to MySQL

This system adopts the MVC design model to improve the analysis efficiency of business English. At the same time, it uses Struts2, Spring and Hibernate frameworks, corpus-based business English research, research on demand analysis of higher vocational business English talent training model research and cross-disciplinary perspective business the design and reform of English teaching system use NoSQL database and MySQL database to realize the storage and management of scientific research information data. This system conducts network and information management of scientific research information, and realizes the four major functions of user management, scientific research project management, scientific research achievement management, and message management.
Automatically Solving Elementary Science Questions: A Survey

Traditionally, Question Answering (QA) has been an important task in the field of Artificial Intelligence (AI) and a substantial amount of research has been done in the past proposing several methods to answer questions in an automated way. The process of QA has been viewed differently by different researchers in the past. Till date, the current / existing systems cannot answer elementary science questions which are obvious and easy to answer even for a primary school student. This paper documents various methodologies proposed in the research area of QA specifically for elementary science domain and includes the discussion of each technique used by various methodologies. We briefly present the overview of three components of generalized QA system and discuss various metrics used for evaluating the QA systems. The paper also presents an exhaustive review of various datasets used for the purpose with their respective description and details. We conclude the paper by identifying the limitations of the methodologies used till date and pointing out some future directions for research in the arena of science QA.
Artificial Intelligence Limitations: Blockchain Trust and Communication Transparency

Natural and artificial systems and agents overcome communication limitations in different ways. It is necessary to develop scenarios for their interaction (for example, in hybrid environments and Industry 4.0), identifying the most risky strategic lines of mutual danger and discovering areas of transparent communication. In this vein, the problem of communication transparency can be understood as a problem of trust. The article discusses the concept of transparency (security, reliability) of communication in the social and technological spheres. This problem has been studied in the specific contexts of artificial intelligence and blockchain. It has been methodologically shown that the combination of these two technologies can significantly expand the capabilities of both. Artificial intelligence is generating more efficient and secure blockchain protocols as a network, as well as private implementations of blockchain solutions such as smart contracts. The probable ecological optimization of the blockchain by artificial intelligence due to the detection of ineffective algorithmic solutions or used software and hardware resources is also important. In turn, blockchain technology provides artificial intelligence with an environment for unfolding and building a transparent history (logging, precedency, patterns, templates). The network nature of the blockchain contributes, on the one hand, to the globality of solutions, the distribution of information, and, on the other hand, to the locality and privacy of owner interests.
A Sparsity-Limitation-Based High-Dimensional Distribution Searching Algorithm for Adversarial Attack

Deep neural networks-based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original inputs. To reduce the impact on human visual sense and to ensure adversarial attack ability, the input image needs to be modified by pixels in considerable iterations which is time consuming. By using sparse mapping network to map the input into a higher dimensional space, searching space of adversarial perturbation distribution is enlarged to better acquire perturbation information. Taking both searching speed and searching effectiveness into consideration, sparsity limitation is introduced to suppress unnecessary neurons during parameter updating process. Based on different eye sensitivity of different colors, maps of each color channel are disturbed by perturbations with different strengths to reduce visual perception. Numerical experiments confirm that compared with the state-of-the-art adversarial attack algorithms, the proposed SparseAdv performs a relatively high attack ability, better imperceptible visualization, and faster generation speed.
McEliece and Blum-Goldwasser group rings based probabilistic cryptosystems

In this paper, we present two probabilistic public key cryptosystems (PKCs) based on the well known algebraic structure of group rings. The first cryptosystem is based on McEliece cryptosystem and we coin it as McEliece-group-ring based PKC. The second PKC is the extension of Blum-Goldwasser PKC in group rings and we name it as Blum-Goldwasser-group-ring based PKC. We carefully discuss the security analysis of the proposed PKCs and their advantages over the existing PKCs. Additionally, we consider the toy examples for both the cryptosystems in order to show the practicality of both the PKCs.
A New Algorithm for Hand Gesture Recognition in Color Videos for Operating System Commands

In recent years, human-computer interaction and machine vision have become two of the favorite research areas in computer science. This paper is research on hand motion and hand gesture recognition using image processing techniques to control some system commands. Different hand motion and gesture recognition methods have been considered by researchers for use in computer systems, video game consoles, and mobile devices. In such cases, hand motion or hand gesture type is detected by tracking the hands' image or by matching the image with gestures storing in the database, after the first position of the hand is identified. In this paper, we provide an efficient way for recognizing and tracking the sequences of image frames. The main objective of this paper is to provide an efficient method for hand recognition in crowded environments, without any restrictions. The frames can be received from a video file. In the first frame, the location of the hand is recognized using color analysis, then it would be compared to the next frames. Detected movements are applied for controlling commands in an operating system. So far, an effective way for this kind of problem has not been suggested. In this study, we have tried to propose an efficient algorithm that is less dependent on background and lighting conditions. We tested the proposed method on several captured videos using the Image Processing Toolbox of MATLAB. These videos are captured in very crowded environments. The presented results showed that in different lighting conditions and various noises, the proposed method works almost without mistakes.
Image Super-Resolution Algorithm based on V-transform Combined with Neural Network

Single image super-resolution aims to increase the size and the visual effectiveness of a low-resolution image. Although existing deep learning-based methods have achieved promising results, they still face great challenges in dealing with the reconstruction of complicated scenes. This is mainly because the deep convolution operations cannot balance the low frequency contents and high frequency details. To mitigate this problem, we propose VTSR, an image super-resolution method based on the V-transform. In VTSR, we propose a VT-block. Change the previous practice of using a convolution layer to directly increase the network dimension to , while the spatial and frequency domain information is parallel, providing richer information for subsequent deep convolution neural networks. Then, we introduce the V-transform, a wavelet-transform, to the super-resolution task to extract richer frequency information. Finally, we designed a loss function mainly for frequency domain information, which has a positive impact on the super-resolution effect and training. Experiments show that the combination of our proposed method and V-transform can achieve better results than most state-of-the-art methods.
Smart Managerial Performance Appraisal System Based on the Quantitative Index and Wavelet Data Mining

This paper studies smart managerial performance appraisal system based on the quantitative index and Wavelet data mining. Data multiplication and division operations and rounding operations are realized simply and quickly through data shift operations, and with this initial setting, it has then brought breakthrough changes to the design of digital systems and greatly promoted the monolithic and automation of digital system design improves the design cycle. More importantly, the wavelet analysis model is integrated to help creating the smart managerial performance appraisal system. Through the comparison simulation, the satisfactory performance is reflected.
Medical image enhancement method based on histogram equalization

Aiming at the problem of uneven gray distribution and low contrast in medical images, the paper proposes a medical image enhancement method based on histogram equalization. The image is initially improved through wavelet transform combined with threshold processing. The detail features of the improved image are extracted by Prewitt operator and fused with the original image, and then the histogram is used to equalize and enhance the image. Experimental results show that the proposed method can better enhance the image feature information and is suitable for a variety of low-quality medical images.
High-Reliability Business Management Strategy Analysis Based on GPRS Wireless Communication

Enterprise is an indispensable factor of production in the country’s economic and social development, and the operation of the enterprise is indispensable. The development of strategic operation of the enterprise is facing many problems, and the survival of the enterprise is facing serious threats. The purpose of this article is to study the method of establishing a reliable enterprise strategic management system based on GPRS wireless communication and neural network. Let the company continue to grow in a sustainable and healthy way. This article puts forward the importance of corporate strategic management under GPRS wireless communication. Strategic business management can improve the foresight and initiative of enterprises, overcome short-term behaviour, and provide a clear direction for the development of the enterprise. In the experimental data of this article, it can be seen from 2016 that the demand for talent management by enterprises is lower than that in 2019. By 2019, enterprises will have the demand is as high as 85.3%, so enterprise management development needs should be taken seriously. The error between the actual output of the network and the expected output is controlled within 5%, which shows that the established neural network has a good evaluation effect and can be used to evaluate the talents of business operators. This also shows that the established BP neural network can fully absorb the judgment experience of experts and the actual employment of enterprises. The results show that the evaluation results obtained according to the evaluation network model have certain guiding significance for the selection and assessment of employers and the self-evaluation of management talents.
Digital Crimes and Our Society

The importance of Digital Crimes per Michael Dennis (2016) is that the computer has become central to government agencies, entertainment, and commerce . defined Cybercrime as "also called computer crime, the use of a computer as an instrument to further illegal ends, such as committing fraud, trafficking in child pornography and intellectual property, stealing identities, or violating privacy. Cybercrime, especially through the internet, has grown in importance as the computer has become central to commerce, entertainment, and government" (Dennis, 2016). According to Dennis (2016), cybercrimes are not targeting an individual's physical body. It targets the virtual body of the individual, which also applies to corporations and government agencies (Dennis, 2016).
Research on Photorealistic Virtual Face Modeling

Most of the traditional face modeling methods adopt the parameter-based method to construct, but the face model constructed by this method is too smooth and ignores the detailed features of the face. To solve this problem, a virtual face modeling method based on 3D motion capture data is proposed in this paper. In order to improve the deformation method for the realistic modeling purpose, this paper divides the modeling process of personalized face into two parts: overall modification and local deformation. The overall deformation modifies the facial shape and the position of the facial features of Maya universal model. Based on the principle of radial basis function interpolation algorithm, a smooth interpolation function is constructed, and the new position coordinates of non-characteristic points are obtained by solving the linear equations, so that they are more in line with the physiological characteristics of human faces. The end result is a more realistic virtual face that you model.
EXISTENTIAL FEATURES OF THE CIVIL LEGAL REGIME OF OBJECTS OF COPYRIGHT AND RELATED RIGHTS

The purpose of this article is to study the essential features of the civil law regime of objects of copyright and related rights, which makes it possible to specify the conditions (criteria) for the protection of the results of intellectual activity at different time periods of historical development and within different legal systems. Moreover, the use of standards for the protection of objects will be analyzed not only in continental copyright and related rights systems, but also in common law countries, where a work is treated not as an idea, but as a thing (commodity) localized in space.
Study on visible-infrared simulation method of water surface target

Infrared simulation software is widely used in the evaluation and model training of infrared equipment. Subject to foreign restrictions, domestic infrared simulation software hardly can meet the current requirements. However, visible simulation software is relatively mature. Thus, generated infrared images from visible images could be another way to replace infrared simulation software. In this paper, a deep learning style transfer method, cycle generative adversarial network, was introduced into the infrared simulation field. We captured visible and infrared images and extracted water surface targets to establish visible-infrared datasets. The cycle generative adversarial network model was applied to generate infrared simulation images from visible images. In terms of visual effect, generated infrared images were close to infrared images. To more holistically evaluate the visual quality of our results, we employ two tactics, structural similarity, and image quality. The result shows that the distribution of generated infrared images was well-matched to the origin visible images. And the image definition and details of generated infrared images were close to infrared images.
Serial Correlation Test and Engineering Application of Partial Functional Linear Variable Coefficient Model Based on Quantile

Checking whether the residual error of the regression model has serial correlation and heteroscedasticity has always been an important work in economic and financial data analysis. In regression models, it is generally assumed that the error terms are independent of each other and have the same variance. If the assumption of independence of noise is broken. For a well-fitted model, the residuals obtained by fitting to be white noise are required, that is, the residuals no longer contain the information of the model. Therefore, the independent homoscedasticity of the error term of the model is a basic assumption. Under this assumption, regular statistical inferences can be made on the model, such as parameter estimation, hypothesis testing, etc., and further forecasts can be made. The utilization rate of engineering applications has increased by 8.3%.
Chinese Translation Errors in English Machine Translation Based on Wireless Sensor Network Communication Algorithm

Globalization is a trend in the future, and the exchanges among countries in the world in economy, politics, and culture are becoming more and more frequent. In order to overcome the increasingly prominent language barriers, translation has become an indispensable “bridge” for communication between countries. Manual translation has long been unable to meet the growing needs of translation. So, machine translation came into being, especially in recent years, machine translation has developed rapidly. Compared with manual translation, machine translation has unique advantages, that is, fast translation speed and free access. With the gradual improvement of the quality of machine translation, the quality of translation has basically met the requirements of rough reading, which makes machine translation one of the hotspots in recent years. However, to achieve the level of intensive reading, the quality of machine translation needs to be further improved. A thorough study of translation error analysis is an important step to improve the quality of machine translation. At present, there are few studies on error analysis of business English in academic circles, and most of them are just simple classification of translation errors, lacking in in-depth empirical research. This study aims to study the advantages and disadvantages of machine translation in translation practice based on wireless sensor network communication algorithms, analyze the typical errors in machine translation, put forward the idea that posttranslation editing can make up for the shortcomings of machine translation, treat English machine translation more objectively, make full use of machine translation tools, combine machine translation with post-manual editing perfectly, improve translation speed, and guarantee translation quality at the same time.
An intelligent generating method for multi-target attacking strategy based on environment-aware deep reinforcement learning

With the development of Deep Reinforcement Learning (DRL), the applications of intelligent decisions in nonsymmetric information games have has become realizable. However, it is still a challenging task in DRL for its difficulties of building efficient exploration and action-reward mechanism, especially in an environment with multiple targets. To address this problem, a generating method of multi-target attacking strategy based on environment-aware DRL is proposed in this paper. Our proposed method consists of two stages in the agent learning process. The first stage is an environmentaware module for predicting the motion trajectories of multiple targets by using the optical flow estimation. The second stage is a decision-making module for predicting appropriate actions such as choosing angles and attacking by using the improved Deep Q-Network (DQN). To solving the problem of sparse rewards in the learning process, the motion trajectories predicted in the first stage are used to build reward trajectories for accelerating the convergence rate of the algorithm in the second stage. The experiments indicate that the proposed method can effectively generate multi-target attacking strategy in our self-built simulation environment. Our method can also provide a novel perspective of intelligent decisions in three-dimension space.
Improving streaming quality and bitrate efficiency with dynamic resolution selection

Dynamic Resolution Selection is a technology that has been deployed by Netflix with its per-scene encoding mechanism applied to VOD assets. The technology is based on a posteriori analysis of all the encoded resolutions to determine the best resolution for a given scene, in terms of quality and bandwidth used, based on VMAF analysis. It cannot be applied to live content, as it would require too much processing power and can't be used in real time.