Reviewerly/exHarmony huggingface dataset:
Research on Express Information Extraction Based on Multiple Sequence Labeling Models

Abstract With the popularity of online shopping, the logistics industry has developed rapidly. Major logistics companies have greater business needs to improve the efficiency of express sorting centers. Therefore, the technology of optimizing the efficiency of the express sorting system in the logistics industry and reducing the cost of time for customers to fill out express orders has received increasing attention from the society. The research work carried out in this paper is based on deep learning using natural language processing technology to extract express single text information, structured extraction of names and mobile phone numbers and addresses. The main content is that the express information structured extraction system automatically extracts information such as name, telephone, province, city, district, and detailed address. The docking of the express delivery sorting business system will help improve the logistics industry's operational capabilities. In this paper, four sets of sequence labeling models are constructed. Through multiple sets of data evaluation, it is shown that the model based on ERNIE and the learning rate decay strategy using Adam optimizer is superior to the current CNN, RNN, LSTM models. The highest F1 of the model constructed in this paper reaches 0.99473.
The Kolmogorov-Smirnov’s Test for Authorship Attribution on the Phonological Level

In the conducted research the experiments have been made with parametric and non-parametric texts for text differentiation. The Kolmogorov-Smirnov’s test has proved the most efficient in authorship identification. The test is more powerful than the Student’s t-test and the chi-square test. The non-parametric two-sample test for revealing significant differences between texts by different authors has allowed us to differentiate the texts of the publicist and the fiction styles. This test has been done in eight consonant groups showing significant differences in all of them. Consequently, the phonological language level is optimal to differentiate styles on. The success rate equals 97%, 98%. The software system has been developed to distinguish between texts by different authors. The cross platform Java programming language has been used. The transcribed texts are stored in the program and make it independent of the Internet.
RNN-based Dialogue Navigation System for Visually Impaired

Helping the visually impaired to walk and guiding him to the destination is a challenging task. The difficulty is to use natural language as the only communication method and assist the visually impaired to see the road. We have developed a conversational navigation system that integrates multiple rounds of dialogue to confirm the destination for the visually impaired. Besides, we also use image interpretation technology based on the RNN neural network to describe the scene in front of the user. Finally, in terms of hardware, we use a low-cost, low- power embedded hardware that integrates cameras, Wi-Fi, and microphones to implement this application.
Digital Image Encryption and Decryption Algorithm Based on Optimization and Fusion Strategy

Digital image chaotic system has the characteristics of large key space and strong anti-plaintext attack, so it has become a hot research topic in recent years. At present, there are still two important problems to be solved. One is that the chaos degree of chaotic sequence is not enough due to improper selection of initial parameters and the other is easy to solve by using chaotic encryption and decryption alone. In this paper, a digital image encryption and decryption algorithm based on optimization and fusion strategy is proposed. Firstly, the parameters of chaos parameters are optimized by genetic algorithm to obtain a better chaotic Logistic mapping encryption relationship. Then, the fusion encryption is carried out by two-level permutation and one-level diffusion technology. Finally, the corresponding decryption algorithm is given in this paper. Through the test of various Performance evaluation indexes, the rationality and validity of the proposed algorithm are verified.electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Multi-class Classification of Bi-lingual SMS using Naive Bayes Algorithm

Short Message Service is the most frequently used source of portable communication via mobile phones. Privacy threats, storage issues, and time wastage have increased due to unappealing messages. Text classification is the only solution to exclude unwanted content. Support Vector Machine and Stochastic gradient descent are commonly used for text classification but they lag in accuracy and upright classification of text messages into multiple subclasses because of several hyper parameters and sensitivity to feature scaling. This results in misclassification and a rise in false-positive and false-negative rates. This paper presents a novel approach to classify English and Roman Urdu text messages using the Multinomial Naive Bayes Algorithm into multiple classes. The proposed classifier achieves better time response because it is less computationally intensive and performs equally well on less training data which makes it suitable to run on android mobile phones. Proposed solution results in 98.87% accuracy for a three-class problem.
Learning Setup Policies: Reliable Transition Between Locomotion Behaviours

Dynamic platforms that operate over many unique terrain conditions typically require many behaviours. To transition safely, there must be an overlap of states between adjacent controllers. We develop a novel method for training setup policies that bridge the trajectories between pre-trained Deep Reinforcement Learning (DRL) policies. We demonstrate our method with a simulated biped traversing a difficult jump terrain, where a single policy fails to learn the task, and switching between pre-trained policies without setup policies also fails. We perform an ablation of key components of our system, and show that our method outperforms others that learn transition policies. We demonstrate our method with several difficult and diverse terrain types, and show that we can use setup policies as part of a modular control suite to successfully traverse a sequence of complex terrains. We show that using setup policies improves the success rate for traversing a single difficult jump terrain (from 51.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate with the best comparative method to 82.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> ), and traversing a random sequence of difficult obstacles (from 1.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> without setup policies to 71.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> ).
Learner’s experience on the use of mobile device for autonomous listening: A narrative inquiry

As the revolutionary approach in education, the use of mobile devices for learning undoubtedly has a great influence on students’ academic success. Many studies have found that these tools are satisfying in terms of authenticity, simplicity, and portability promoting students’ learning autonomy where they can study anywhere and anytime. Using the lens of narrative inquiry, this study explored a student's experience in using mobile devices for autonomous listening activities and investigated the benefits of these activities for her language competences. Interview and narrative frame proposed by Barkhuizen and Wette (2008) were used to answer the research questions. The findings showed that the learner mostly did extensive listening activities by utilizing a large amount of understandable and pleasurable spoken expressions through her devices. Having sophisticated software and great immersion, these tools allow the learner to get massive listening input which develops her vocabulary and basic comprehension. Different from analog tools used in her first listening experience, these tools devote thousands of listening applications and provide thousands of listening sources like live streams, English songs, news, or videos that can be accessed effortlessly. However, it is also revealed that these tools also affects the learner's psychological factors; learning motivation, confidence, and self-efficacy.
Determining new way to detect hydrocarbon polluted water at vertical and horizontal spaces

Remote and proximal sensing are important tools to discover surface and ground natural resources such as water, wealthy minerals, and hydrocarbons. The results of this study rely on discovering the ability of water detector Aqua to detect many products of hydrocarbons and polluted water with hydrocarbon at different vertical and horizontal spaces on and under the ground. The importance of this discovery is helping to sense and detect the accurate location and underground depth of hydrocarbons and water pollution with hydrocarbons as quickly and efficiently as possible by using Aqua device. Many laboratory experiments were carried out by Aqua on different gaseous, liquid, and solid hydrocarbon products. Field experiments were achieved to test the ability of the device to detect polluted water with hydrocarbons at different distances and depths. Verification of the results was also carried out when measuring hydrocarbon concentration of several samples at the laboratory via the analytical method, relationship between lab measurements of these samples and outcomes of Aqua has significant correlation and determination.
Asymptotic results with estimating equations for time-evolving clustered data

We study the existence, strong consistency and asymptotic normality of estimators obtained from estimating functions, that are p-dimensional martingale transforms. The problem is motivated by the analysis of evolutionary clustered data, with distributions belonging to the exponential family, and which may also vary in terms of other component series. Within a quasi-likelihood approach, we construct estimating equations, which accommodate different forms of dependency among the components of the response vector and establish multivariate extensions of results on linear and generalized linear models, with stochastic covariates. Furthermore, we characterize estimating functions which are asymptotically optimal, in that they lead to confidence regions for the regression parameters which are of minimum size, asymptotically. Results from a simulation study and an application to a real dataset are included.
cVM: Containerized Virtual Machine

Virtual machines (VM) and containers are virtualization technologies that partition computing resources and isolate workloads. They are the foundations for resource consolidation which leads to the success of cloud computing. VMs perform partitioning and isolation at the machine device level while containers do so at the operation system level. The pros and cons of VMs and containers are generally well understood. VMs provide better isolation and security while containers are less resource intensive and perform better. There are various attempts to address the shortcomings in both communities to narrow the gap between the two. In this paper, we review these efforts and discuss their strengths and weaknesses. We also present the cVM architecture with ideas to explore ways of reducing VM resource demand and improving VM performance towards being comparable to those of containers. cVMs bootstrap from a “disk template” where Linux Logical Volume Manager snapshot is used to allow cVMs to share read-only files and directories, thus reducing their disk footprint. Similarly, “memory template” allows cVMs to share read-only code and data memory pages, thus reducing their memory footprint. Finally, cVMs leverage device hotplug capability and lightweight Linux distributions to reduce bootup time without sacrificing too much generality. And cVMs also take advantage of device virtualization and utilize device passthrough to achieve near native I/O performance.
Denoising of images using Thresholding Based on Wavelet Transform Technique

Abstract This paper presents an efficient method based on thresholding for images that are corrupted due to Gaussian noise. In order to achieve this task, wavelet transforms and various thresholding techniques have been applied. But the selection of the thresholding technique is restricted due to their widespread use in image denoising application. In this paper, a more efficient thresholding scheme named as neigh shrink sure is studied by incorporating the neighboring wavelet coefficients. Different thresholding techniques like Bayes shrink and Neigh shrink sure algorithms are applied to different images. The results are obtained using 3 different wavelets db-4, sym-4, and coif-4. It has been observed that the use of coif-4 transform produces better results as compared to other wavelet transforms. Further, it has been analyzed that Neigh Shrink sure method performs better in de-noising of corrupted images in comparison to Bayes shrink thresholding technique by possessing higher Peak Signal-to-Noise Ratio (PSNR) and lower Mean Square Error (MSE).
Human Motion State Recognition Based on Multi-input ConvLSTM

Abstract The human body generates acceleration signals during movement. After collecting and processing this signal, the movement state of the human body can be analyzed and the behavior of the human body can be judged. Human motion state recognition has a wide range of applications in fields such as health monitoring, somatosensory games, and user social behavior analysis. In this paper, the UCI data set collected by mobile phone sensors is used to build the model using convolutional long and short-term memory neural network (ConvLSTM) combined with multi-input CNN (Multi-head CNN), and combined with long and short-term memory neural network (LSTM) and convolutional memory Neural network (ConvLSTM) for comparative evaluation. The accuracy of the model in this paper reached 93.75%. Experimental results show that the new algorithm can more accurately classify and recognize the human motion state.
Automatic Sparsity-Aware Recognition for Keypoint Detection

We present a novel Sparsity-Aware Keypoint detector (SAKD) to localize a set of discriminative keypoints via optimization of group-sparse coding. Unlike most of current handcrafted keypoint detectors that are limited by the manually defined local structures, the proposed method has the capacity to allow flexibility for exploiting diverse structures with the combination of visual atoms from a vocabulary. Another key valuable attribute is that its group-sparsity nature concentrates on discovering sharable structural patterns across keypoints within an image jointly. This main merit facilitates to localize repeatable keypoints and resists against distractors when image undergoes various transformations. Extensive experiments on four challenging benchmark datasets demonstrate that the proposed method achieves favorable performances compared with state-of-the-art in literature.
ChainSim: A P2P Blockchain Simulation Framework

In the past few years, blockchains have been one of the most attractive emerging technologies. Many researchers and institutions have devoted their resources to the development of more effective blockchain technologies and innovative applications. However, with the limitation of computing power and financial resources, it is hard for researchers to deploy and test their blockchain innovations in a large-scape physical network. In this paper, we design a peer to peer blockchain simulation framework to address this challenge, called ChainSim. ChainSim provides a foundation and skeleton to argument and simulate as large as thousand-nodes P2P blockchain network with a single computer. This paper presents ChainSim basic structure and simulation mechanism; and showcase ChainSim capabilities and usefulness. With ChainSim, researchers can test their new consensus protocols, reproduce a subtle security attack and evaluate its risks with a large number of nodes under heavily transaction loads.
Understanding Videos at Scale: How to Extract Insights for Business Research

Video content has become a major component of total internet traffic. Growing bandwidth and computational power conspire with an increasing number of video editing tools, smartphones, and online platforms that have facilitated video production, distribution, and consumption by businesses and consumers alike. This makes video content relevant across business research disciplines. However, analyzing videos can be a cumbersome manual task. Automated techniques are scattered across technical publications and are often not directly accessible to business researchers. This article synthesizes the current state of the art and provides a consolidated tool to efficiently extract 109 video-based variables, requiring no programming knowledge. The variables include structural video characteristics such as colorfulness as well as advanced content-related features such as scene cuts or human face detection. The authors discuss the research potential of video mining, the types of video features of likely interest and illustrate application using a practical example.
Oversampling Algorithm Based on Reinforcement Learning in Imbalanced Problems

The imbalanced problem indicates that the data set is unevenly distributed, resulting in sub-optimal classifiers to recognize the minority class. Traditional solutions try to design new classifiers to solve this problem or balance the skewed data sets, the former is too costly while the latter has an uncertain effect on different combinations of classifiers and measurements. In this paper, we propose a reinforcement learning-based oversampling method, which can directly produce targeted samples according to the downstream classifiers and measurements. During training, our learning procedure introduces the classification information to the generation process. Moreover, as opposed to oversampling approaches, we have no assumption of the downstream classifiers and performance metrics, and the proposed has a wider application. We carry out experiments on 17 UCI and KEEL data sets, experimental results demonstrate the superior performance of our proposed method.
Optimization of request processing times for a heterogeneous data aggregation platform

Abstract A heterogeneous data aggregation system, e.g. developed within the frame of the GRADLC project, allows for a flexible expansion by connecting new data storages, as well as providing researchers a fast and aggregated access to heterogeneous data from independent (astroparticle physics) projects, while reducing the load on the original data storages. However, this flexibility requires balancing user requests in the queue with respect to various request processing times for the distributed storages, taking into account the different data processing policies on each particular storage. In order to attack this problem, a mathematical model of the data aggregation system was developed, and approaches to optimization of the request ordering in the processing queue are proposed and investigated by performing a numerical experiment. Based on this results, a job shop scheduling algorithm was revealed which gives benefit in mean request processing times compared to the well-known first in, first out (FIFO) model.
THE APPLICATION OF RSA AND LSB IN SECURITY OF MESSAGES ON IMAGERY

In this study is to discuss cryptography and steganography where the function is to insert a message or text into an image with JPG extension, the text to be inserted into the image has been encrypted using the RSA method so that the file is safer to be inserted into images, messages that are inserted into a blue image, this application aims to secure a message that you want to save, this application is made made using Android Studio and can be run on a mobile phone.
Secure Agritech Farming Using Staging Level Blockchaining and Transaction Access Control Using Micro QR Code

In this day and age, ongoing business arrangements request an effective way to deal with address complex situations and address security dangers. This has offered path to the blockchain structure which guarantees information security and equal access to information vault by all partners contrasted with the customary procedure. The Blockchain can possibly adequately addresses the present issues looked by the rural division utilizing an incorporated circulated organize. Blockchain is utilized to guarantee any exchange done either in item or administration applications are made sure about and put away in an archive that can be gotten to by nearly anybody. Blockchain can manage the issues winning in the horticulture part calm. The present paper proposed to comprehend the issues winning in the horticulture segment and how innovation can be utilized to take care of the issue in the general cultivating process utilizing a unified circulated organize worldview BLOCKCHAIN.
Analysis of the relationship between the choice of the color model for the representation of images and the efficiency of solving the problem of improving the image resolution

Abstract The article discusses the influence of the choice of a color model for representing a raster image on the efficiency of increasing its resolution using an artificial neural network. The article begins with the essence of the research. After that, the process of obtaining for training networks with an identical topology of a dataset using different color models of image representation, namely RGB, HSV, L * a * b *, NTSC and YCbCr, is described. After that, an assessment of the effectiveness of trained neural networks on the previously described datasets is given. To assess the performance of an artificial neural network, two algorithms are used: SSIM and PSNR. As a result of the assessment, the networks using RGB and YCbCr color models showed the highest results. At the end of the article, there are reflections on the reasons for this result.
Group Key Management in Cloud for Shared Media Sanitization

Cloud provides a low maintenance and affordable storage to various applications and users. The data owner allows the cloud users to access the documents placed in the cloud service provider based on the user's access control vector provided to the cloud users by the data owners. In such type of scenarios, the confidentiality of the documents exchanged between the cloud service provider and the users should be maintained. The existing approaches used to provide this facility are not computation and communication efficient for performing key updating in the data owner side and the key recovery in the user side. This paper discusses the key management services provided to the cloud users. Remote key management and client-side key management are two approaches used by cloud servers. This paper also aims to discuss the method for destroying the encryption/decryption group keys for shared data to securing the data after deletion. Crypto Shredding or Crypto Throw technique is deployed for the same.