Reviewerly/exHarmony huggingface dataset:
Design of Mechanical Creeper for Disabled Persons

A mechanical creeper trolley is a tool that is used by vehicle engineers who repair or inspect the vehicle in the automotive industry. It has a low profile wheel frame attached to a platform on which users can lie. Mostly mechanical creepers are used to gain access to the underside of a vehicle in order to perform maintenance of the vehicle. Certain forces needed to move the creeper and it is only accessible for a normal person. Usually, a semi-disabled person is not able to provide this much force. The intention of this manuscript is to propose a more promising automatic mechanical creeper for semi-disabled persons to facilitate easy mobility and gain easy accessibility, by altering a conventional mechanical creeper with a gear system and console box. The rack and pinion gear system leads to the conversion of rotatory motion into linear motion and acts as an actuator. This aids in providing additional movement for the creeper. A caster and roller wheel provides flexible movement to the creeper. The console system used enables automatic control of creeper. Alterations made in the existing mechanical creeper would be a boon to the semi-disabled persons and also highly useful for the normal vehicle engineers to have easy accessibility and enables many directions of movement during vehicle inspection and maintenance.
Shopping Around: CoSurvey Helps You Make a Wise Choice

When shopping online, customers usually compare commodities with each other before making their purchase decision. In addition to the product price, they also concern the word-of-mouth. However, marketing strategies from various e-commerce platforms, along with the diverse online commodities, make it difficult for customers to distinguish the most cost-effective products. Present cross-platform commodity comparison applications merely focus on product prices, without jointly concerning the reviews. In this demonstration, we developed a web-based application, CoSurvey, which matches commodities from various e-commerce platforms and analyzes product comment sentiment on the base of the proposed Attention-BiLSTM-CNN Model. The model uses an attention-based Bi-LSTM network to learn sentence sequence information, uses a CNN to learn sentence structure information, and uses a multilayer perceptron (MLP) to learn meta-information. The meta-information in the comment sentiment analysis task includes comment’s like number, reviewer level, additional image, deliver time, and sentence length. Besides the keyword query, CoSurvey provides customers a survey of cross-platform products price changing trends and comment sentiment evolutions. The high concurrency requirements and load balance are also concerned.
LIBRARY TRANSFORMATION IN MODERN SOCIETY: PRO ET CONTRA

The capacities for a library transformation in the contemporary society are considered. Various innovative forms of organization of library space (multimedia, information and educational centers, creative laboratories, etc.) are presented.
Research on Innovation-Profession Integration Skills Compass Design under Big Data

The advent of the big data era has brought about radical changes in all aspects of society. In this paper, theoretical system for the investigation and evaluation of entrepreneurial talents' abilities in the innovation-profession integration education is studied by big data. Focusing on the analysis of the existing difficulties in the lack of top-level design in innovation-profession education, this paper puts forward a skills compass model of innovation-profession skills based on big data technology to provide research value for the establishment of entrepreneurial talent training and evaluation system.
Exploration of College English and Computer Professional English Teaching

College English and computer professional English are both language courses, while college English is a compulsory course, which has great influence on computer professional English courses. In view of the existing problems in college English teaching and computer professional English teaching, this paper puts forward that college English teaching must be changed from general English to academic English. On the basis of massive open online course (MOOC) teaching reform, the author puts forward a new teaching system of college English and computer professional English, that is, college English teachers and professional English teachers jointly build an open college English online course platform. Teachers need to pay attention to the use of online course platform when giving lectures. The construction of English online course group is urgent. It will become a new trend of teaching and an effective way to improve the teaching quality and speed up the teaching progress.
CAT-BERT: A Context-Aware Transferable BERT Model for Multi-turn Machine Reading Comprehension

Machine Reading Comprehension (MRC) is an important NLP task with the goal of extracting answers to user questions from background passages. For conversational applications, modeling the contexts under the multi-turn setting is highly necessary for MRC, which has drawn great attention recently. Past studies on multi-turn MRC usually focus on a single domain, ignoring the fact that knowledge in different MRC tasks are transferable. To address this issue, we present a unified framework to model both single-turn and multi-turn MRC tasks which allows knowledge sharing from different source MRC tasks to help solve the target MRC task. Specifically, the Context-Aware Transferable Bidirectional Encoder Representations from Transformers (CAT-BERT) model is proposed, which jointly learns to solve both single-turn and multi-turn MRC tasks in a single pre-trained language model. In this model, both history questions and answers are encoded into the contexts for the multi-turn setting. To capture the task-level importance of different layer outputs, a task-specific attention layer is further added to the CAT-BERT outputs, reflecting the positions that the model should pay attention to for a specific MRC task. Extensive experimental results and ablation studies show that CAT-BERT achieves competitive results in multi-turn MRC tasks, outperforming strong baselines.
Generative Adversarial Network for Heritage Image Super Resolution

In this paper we have proposed Generative Adversarial Network (GAN) based Super resolution reconstruction (SRR) method for the estimation of high resolution (HR) heritage images. The proposed SRR method via the GAN model (SRRGAN) is modeled as a min-max optimization process by integrating some new loss functions which will enable to maintain spatial and textural homogeneity in the estimated HR images. Initially, we have divided the input test image into Textured and Non-Textured patches based on their inherent characteristic followed by the optimization of the GAN model via loss functions $$loss_{TeXP}$$ and $$loss_{NonTeXP}$$ respectively to yield textured and non-textured patches of the HR image. Loss function $$loss_{TeXP}$$ helps to preserve realistic and diverse textural content of heritage images along multi-scale and multi-orientations. Whereas, $$loss_{NonTeXP}$$ efficiently preserves the complete high-frequency map of the heritage images. Consequently, the estimated HR images preserve sufficient image details with realistic textural content and reduced artifacts compared with the existing state-of-the-art SRR methods.
IQINN: Improve the Quality of Image by Neural Network

The proposed paper deals with image quality improvement. In many applications, images play noteworthy roles. In the application of pathology, satellite imaging better excellence of the image is very vital. In recent days, there are numerous methods that are used to increase the quality of the image. There are different noise removing algorithm are available such as homogeneous filter, Gaussian filter, median filter, and bilateral filter. Such kinds of algorithms improve the excellence of the image. The proposed work is done with the help of neural network architecture. The ideal is to work on seven-layer neural network architecture. The model is light weighted. The model gets analyzed by changing the parameters. The performance of image quality is evaluated through peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index (SSIM). It is noticed that the proposed model works well.
Krasnoyarsk Musical Theatre Orchestra in the Context of Modern Musical and Theatrical Performance

The paper reveals the role of Krasnoyarsk Musical Theatre orchestra as an integral part of theatrical performance. The orchestra’s activity is analysed in the context of modern musical and theatrical art. Scientific originality of the study lies in the fact that the orchestra is considered as a full-fledged member of the theatre troupe. The researchers’ attention is focused on the following issues: the orchestra’s current activity, the staff, the conductors’ influence on the repertoire policy. The research findings are as follows: the authors prove that Krasnoyarsk Musical Theatre orchestra’s activity contributes largely to musical performance development in Krasnoyarsk region.
Security of Digital Images Based on Stream Cipher and MDS Matrix

Stream ciphers are extensively used over a wide range of applications including security of digital data. In this paper, a method for securing different types of images (binary, gray scale, true color and index) based on stream cipher (RC4A) and MDS (Maximum Distance Separable) matrix is proposed. The proposed scheme is based on the cryptographic Permutation-Substitution Network (PSN) and hence achieves Shannon’s confusion-diffusion characteristics required for a robust encryption algorithm. The scheme encrypts a digital image into a random-like image from human visual as well as statistical point of view. Several encryption evaluation metrics are applied on test images to empirically assess the performance and efficiency of the proposed method. The consequences of these statistical and security tests support the concreteness of the proposed approach.
Assistance to young people in choosing a profession on the example of the activities of youth clubs in St. Petersburg

One of the most important decisions that a person makes in their life is the choice of profession and career path, which will determine the quality of their entire life. A young person should choose a future profession, an appropriate educational institution, and be prepared for possible changes in the path of their professional development in connection with the General socio-economic changes in the country. This article is devoted to this problem.
Sub-pixel Edge Contour Detection Algorithm Based on Cubic B-Spline Interpolation

It[aut]Cao, Jianzhao was[aut]Ma, Ruwei proposed[aut]Pang, Renning, in[aut]Qi, Yuanwei this paper, an algorithm based on machine vision to deal with the problems of low efficiency and large error in profile detection of industrial steel plates. The image filtering operation is used to remove the noise of the steel plate picture, and a method for judging the image filtering effect is proposed. And an image segmentation method combining OTSU and Canny algorithm is designed to achieve dynamic segmentation of steel plate images to obtain the best segmentation effect. In order to fit steel plates with head and tail deformation or camber, a sub-pixel edge fitting method based on cubic B-spline interpolation was proposed to obtain the sub-pixel coordinates of steel plate contour, which provides data basis for crop shearing. The experimental results show that this method has high detection speed and precision and can meet the actual production needs.
SCHC: Incorporating Social Contagion and Hashtag Consistency for Topic-Oriented Social Summarization

The boom of social media platforms like Twitter brings the large scale short, noisy and redundant messages, making it difficult for people to obtain essential information. We study extractive topic-oriented social summarization to help people grasp the core information on social media quickly. Previous methods mainly extract salient content based on textual information and shallow social signals. They ignore that user generated messages propagate along the social network and affect users on their dissemination path, leading to user-level redundancy. Besides, hashtags on social media are a special kind of social signals, which can be regarded as keywords of a post and contain abundant semantics. In this paper, we propose to leverage social theories and social signals (i.e. multi-order social relations and hashtags) to address the redundancy problem and extract diverse summaries. Specifically, we propose a novel unsupervised social summarization framework which considers Social Contagion and Hashtag Consistency (SCHC) theories. To model relations among tweets, two relation graphs are constructed based on user-level and hashtag-level interaction among tweets. These social relations are further integrated into a sparse reconstruction framework to alleviate the user-level and hashtag-level redundancy respectively. Experimental results on the CTS dataset prove that our approach is effective.
An Entropic Approach to Assess People’s Awareness of the Health Risks Posed by Pesticides in Oenotourism Events

Wine production and vineyard work are seasonal activities as there is a period when no intervention is required and oenotourism may fill that void. On the other hand, given that grapevines are tied to this area of activity, it is of paramount importance to assess customer and staff awareness of the health risks of pesticide use, that emerge from people's responses to specific questionnaires. Therefore, a workable problem-solving method is proposed which enables one to assess the level of awareness of people who are taking on the risks, evaluated in terms of an estimation of the individuals entropic state with respect to this particular issue. The analysis and development of such a model is based on a number of Logic Programming formalisms for Knowledge Representation and Reasoning, tha are consistent with an Artificial Neural Network approach to computing. The data collection process involved 173 participants. The proposed system presents an accuracy of about 90% and enables the diagnosis of risk awareness and the correspondent fragilities among customers and staff for a particular pesticide.
Exploring Color Models for Enhancement of Underwater Image

Image enhancement is already a challenging process and is further intensified due to image degradation by several factors like absorption, reflection, bending and scattering of light. The light is attenuated while travelling underwater thereby causing different wavelengths of light to have varied attenuation rates. As the blue and green color have shorter wavelengths, these colors are prevalent in underwater images as compared to other colors. In this paper, we explore different color models for enhancement of underwater image using CLAHE followed by Guided filter. Initially, CLAHE is applied on the image to enhance the colour contrast. Secondly, the Guided filter is applied on the resultant image to further enhance the contrast and resolve lack of lighting.
Design of social media-based information systems for higher education alumni

Abstract This study aims to produce information systems design and alumni data of Universitas Negeri Jakarta (UNJ) based on social media that can be accessed anywhere, facilitate the processing of alumni data, store alumni data and be able to convey information to alumni. This research emphasizes the development of information system designs whose output is user requirements and use case diagrams that can be implemented into social media-based information systems. The method used to formulate user requirements is through survey techniques and interviews with several alumni and relevant stakeholders, then the data obtained are analyzed qualitatively to be synthesized into the final user requirements. From the results of the study it can be concluded that the alumni information system is considered to be very effective in attracting alumni who have been scattered. The habit of Indonesian people who often use social media to publicize their activities unconsciously has provided information about themselves to the wider community, which can be used as a database that can be used for the benefit of the campus.
Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients

This study proposes a new image compression technique that produces a high compression ratio yet consumes low execution times. Since many of the current image compression algorithms consume high execution times, this technique speeds up the execution time of image compression. The technique is based on permanent neural networks to predict the discrete cosine transform partial coefficients. This can eliminate the need to generate the discrete cosine transformation every time an image is compressed. A compression ratio of 94% is achieved while the average decompressed image peak signal to noise ratio and structure similarity image measure are 22.25 and 0.65 respectively. The compression time can be neglected when compared to other reported techniques because the only needed process in the compression stage is to use the generated neural network model to predict the few discrete cosine transform coefficients.
Terminal Control Researching with Application of the Key Points to Assess the Defined Problem Realization

In article it's made the analytical review of articles in which methods and algorithms of terminal control are considered. Terminal control - coordinated movement of a mobile robot (MR) in time and space. The purpose of the terminal control problem solution is searching of such movement control of the mobile object, which provides achievement of the target position from the initial point in a given time with the given tolerance. Since the environment is uncertain, a heuristic approach to solving the problem of terminal control together with automatic motion control of the MR is applied. This paper proposes the application of intelligent evaluation of the terminal state of MR. This is the difference between solving the problem of terminal management with the proposal to introduce into the environment of movement of control (reference) areas (points). At each control point, the parameters of the further movement of the MR are calculated. The method of key areas definition in the motion environment allowing to lower the indeterminacy degree during the solution of the terminal control problem is developed. Empirical researches of the proposed method that showed the effectiveness of the proposed approach to the solution of the terminal control problem are conducted.
Adaptive Integer Linear Programming Model for Optimal Qubit Permutation

Editor's notes: Quantum computer performance is often limited by the maximum number of pairs of qubits that can interact. This article expresses the problem in terms of integer linear programming, showing that in this way we can identify an optimal solution. -Marcelo Lubaszewki, Universidade Federal do Rio Grande do Sul -Matteo Sonza Reorda, Politecnico di Torino.
Application of Piecewise Linear Approximation of the UAV Trajectory for Adaptive Routing in FANET

A significant problem of routing protocols in the Flying Ad Hoc Networks (FANET) is a significant overhead cost due to the high mobility of networking nodes.The problem is caused by a need to send information messages about locations of unmanned aerial vehicles (UAVs).In order to reduce the amount of service information, the following trajectory approximation algorithms have been investigated: an algorithm for conjugating courses and an algorithm based on continuous piecewise-linear functions (CPLF).Four modifications of the CPLF-based algorithm are considered, which differ in the type of piecewise linear function used: basic CPLF, generalized CPLF, generalized CPLF with a compact notation form, and adaptive CPLF.The disadvantages of each algorithm are analyzed.The CPLF approximation of a fragment of an aircraft trajectory consisting of two straight sections and a curved section with variable steepness between them is performed.It is established that adaptive CPLF with variable step reduces the error of trajectory approximation due to the location of most points on the curved sections of the aircraft maneuvering.The modified version of ADV routing protocol has shown a lower overhead value (the gain for small pause time values reaches 23 %).Thus, the effectiveness of the proposed approximation-based routing in FANET is shown.
A Turn-back Fault Diagnosis Method for Urban Rail System Based on Spark

Urban rail transit industry has accumulated a mass of data about intercity railways. There are three types of turnback fault in urban rail transit industry data: automatic turnback, end changed automatically, and end changed intermittently. The three type of faults may cause serious accidents, which will damage people's life. However, the end changed automatically and the end changed intermittently are getting much less focus than the automatic turn-back. Recognizing those faults can improve the managers' working efficiency of urban rail transit and protect the safety of passengers. The Apriori algorithm has been frequently used for fault diagnosis under different circumstances, helping managers make effective decisions based on the turn-back fault association rules hidden in the past data. Spark, a fast and general-purpose computing engine, is designed for large-scale data processing. In the era of big data that the amount of data increases exponentially, the memory calculation method of Spark can improve the computing efficiency of the Apriori algorithm. In this paper, we resort to the Apriori algorithm based on Spark implementation to mine feature combinations that appear in message data frequently when faults occur in automatic turnback, end changed automatically, and end changed intermittently respectively.
Ranking Decision Making Units with the ideal and anti-ideal points

This paper introduces two virtual Decision Making Units (DMUs) called ideal point and anti-ideal point, Then calculates distances of each DMU to the ideal and anti-ideal point. The two distinctive distances are combined to form a comprehensive index called the relative closeness (RC) just like the TOPSIS approach. The RC index is used as an overall ranking for all the DMUs. Then, this method compares with AP [1], Wang et al. [8], and Wu [9] methods. The proposed method is more simple and better than other methods and also it doesn’t have drawbacks of the previous ranking methods.
Study of Cloud Computing Business Framework by Pre-cloud recognition

Abstract The current study strongly analyzes the factors that determine the acceptance of cloud companies (SASS model), and this strategy to do their job. The research model is designed to explore factors that affect the use of computers, including host technology (TAM) models and other external such as organizational size and technical complexity. Data compiled from 200 companies are used to test the ideas. The results of this study show what important factors need to be considered and how they relate to each other. They highlight the needs of companies, especially those related to cloud operations, for those who want to use a real digital governance model. These technologies and technology-related applications that use virtual technology and operate in a distributed network that is accessible to sites and social networking sites. Developed and upgraded to cloud computing systems, cloud grid and computers.