Reviewerly/exHarmony huggingface dataset:
A Protected Cloud Information Sharing Convention for Big Business Supporting Various Leveled Catchphrase Search

Distributed storage turns into the need for putting away and sharing information for big business clients. Scrambling before transferring information to the cloud is the best appreciation to defending organisation favored experiences, in any case, it forestalls the invaluable technique on plaintexts, like exploring the cloud data. Furthermore, staff in the partner endeavor has different layers structures and the following layer laborer have some of the the advantage to notice the bottom layer representatives' information to check whether these clients abuse the guideline while not holding the specialist's recollect of. private and public cryptography with watchword search could be a notable cryptanalytic crude fitting for secure distributed storage, that upholds catchphrase search while not coding public and private key cryptography Unfortunately, no current public and private key encrypted with data search topic upholds the watching execution while not approval from the To deal with this issue, we to will, in general, propose a variation of public-key encryption with keyword search named hierarchic Public Key cryptography with Catchphrase Search and supply a semi-conventional development using a public key tree and public encryption key with the data search.
Control Strategy and Flight Trajectory Optimization Strategy Based on Improved De Casteljau's Algorithm for Indoor Drone

In this paper, we introduced the control strategy of indoor drones and the optimization strategy for UAV flight. For the flight control of the UAV, we use a dual closed-loop control system. Specifically, it is to control the UAV to reach the desired position and achieve the desired attitude by setting the dual closed loop position loop and attitude loop. In order to allow the drone to safely and stably pass the unguided points in the set trajectory, we have improved the traditional De Casteljau's algorithm so that it can be better applied to the predetermined trajectory of the drone. Specifically, this improved algorithm selects different numbers of control points to construct the Bézier curve in sections according to the actual situation. Finally, a complete optimization strategy is obtained and we have proved through experiments that the improved De Casteljau's algorithm can indeed reduce drone flight errors and make the flight trajectory smoother and more stable.
Object Recognition and Tracking Using the Particle Estimator

In this chapter we describe the particle estimators and its effectiveness for tracking objects in video sequences. The particles estimators are specifically advantageous in transition state models and measurements, especially when these are non-linear and not Gaussian. Once the target object to follow has been identified (in position and size) its main characteristics are obtained using algorithms such as FAST, SURF, BRIEF or ORB. As the particle estimator is a recursive Bayesian estimator, where observations update the probability of validating a hypothesis, that is, they use all the available information to reduce the amount of uncertainty present in an inference or decision problem. Therefore, the main characteristics of the object to follow are those that will determine the probability of validating the hypothesis in the particle estimator. Finally, as an example, the application of a particle estimator is described in a real case of tracking an object in a sequence of infrared images.
A Logic-based Multiagent Product Configuration Model

Product configuration is an essential task in manufacturing customised products and is becoming more important as companies move to collaborative manufacturing, collaborative procurement, and collaborative inventory, to offer faster and more cost effective services and products. Hence the need for proactive complex distributed systems. Modelling Self-managing Multi Agent Systems (MSMAS) is a software development methodology that facilitates the design and development of complex distributed systems based on the multi agent systems paradigm. In this paper, we propose a model for product configuration systems using the MSMAS methodology. We utilise MSMAS system norms to model a novel negotiation protocol, and model the life cycle of these norms as non-atomic activities. We also formally express them as Event Calculus (EC) theories. In addition, we formally describe how to find both partial and complete solutions for the product configuration problem, and how conflict can be resolved.
Nonlinear feature extraction method of pitch control signal in Erhu works based on graph convolutional neural network

Feature integration refers to the process of integrating feature vectors in a short time window into a new single feature vector in a long time window. The intonation of erhu works is a long-term temporal feature. At present, the mean and variance are often used to obtain the long-term characteristics of intonation in erhu works to maintain the temporal characteristics of timbre. On the basis of graph convolutional neural network, the non-linear feature extraction method of intonation control signal in erhu works is studied. Using the smallest unit note of music as the processing unit, a dynamic multivariable automatic regression model feature is proposed, which realizes the timing feature of timbre Integration. This feature vector is applied to the timbre recognition of musical instruments. At the same time, in the post-processing of the intonation recognition in the Erhu works, the method of calculating the probability of the weighted average is used to determine the category of the music, which has greatly improved.
Research on the Online Evaluation System of Modern Apprenticeship Based on the Internet

The modern apprenticeship system has achieved outstanding results in the cultivation of talents after years of practice, and evaluation is an important part of the modern apprenticeship system. In the development of the apprenticeship, the evaluation methods have always been flexible and diverse, but basically, they are still at the stage of offline evaluation. With the prosperity and development of online education, how to solve the various drawbacks brought about by the separation of time and space, and how to conduct online evaluation, it has gradually become the focus of research for educators. This paper elaborates and summarizes the relevant research background and theories, and puts forward student-centered suggestions for the selection of software and platform for evaluation. Assessors should not choose to require too high hardware and software when conducting online assessments, to facilitate students' online assessments.
Towards a Systematic Socio-Intentional Framework for Agile Methods Tailoring

Agile has become one of the most popular software development approaches thanks to its flexible and evolutive features. To find further suitable practices, teams start to follow the tailoring approach by choosing only the fragments of different methods that fit their needs and context. Many tailoring approaches have been proposed by orienting different aspects such as process, resource and goal. While the interaction between team members is very important in agile methods, none of these approaches focuses on the socio-intentional aspect. In the literature, we can find many case studies that link socio-intentional aspects to the tailoring of agile practices. Even though it is helpful to know it, locating relevant information can be effort and time-consuming. This research proposes a socio-intentional framework that can analyze agile practices and indicate how to tailor them with the help of an evidence-based tool and a modeling language. This framework will allow practitioners to identify the right practices to achieve their goals and analyze their suitability and vulnerability. It will also indicate how to successfully implement them in the software development process.
Research on the Construction of University Music Teaching Cloud Platform Based on Data Mining

As an indispensable medium between teaching theory and teaching practice, teaching mode can not only effectively guide teaching activities in theory, but also formulate effective operation procedures and implementation countermeasures for specific teaching practice. With the continuous development of educational informatization, the application of cloud computing technology in universities, especially in the development and construction of teaching resources platform, is becoming more and more extensive. With the establishment and improvement of online education platform, online education will soon become an important way for students to learn. Information-based education not only brings convenience to teachers and students, but also stores a lot of information generated when teachers and students apply the platform. Therefore, it is of great theoretical and practical significance to explore the construction of music performance teaching resource platform based on cloud computing under the background of educational informatization development. Based on the analysis of the construction of university music teaching cloud platform, this paper puts forward a university music curriculum model based on cloud computing and data mining technology.
Review on Digital Image Processing Using Image Retrieval and Recognition

Image processing is one of the vast growing scenarios. From various resources this method is used to improve raw images received from satellites, space probes, aircrafts, military reconnaissance flights or pictures taken in normal day-to-day life from normal cameras. It is a kind of signal processing. This paper is an overview of image processing methods.
Application of Image Segmentation Based on Deep Learning in Mobile Terminal Equipment

Currently, ultrasound image segmentation on mobile terminal has two limitations. First, the images collected by portable ultrasonic devices are of low quality and poor quality, which makes it difficult for traditional digital image processing methods to segment the target region accurately and stably. Second, the limited resources of mobile terminals make it difficult for deep learning models to operate efficiently on mobile terminals. Therefore, this paper improves the image segmentation model and applies it to portable ultrasonic equipment after compression. Firstly, the MobileNet V2 structure was used to replace the deep network of feature extraction in the Deeplabv3 + network model, so as to make it lightweight and reduce the parameters and calculation amount. Then, the model after training was quantified and compressed. Finally, the compressed model was applied to the neck artery image segmentation in portable ultrasound equipment. The direction of blood vessel and the Angle of line deflection are calculated from the segmenting images, and the automatic adjustment of color Doppler sampling frame is realized by using the parameters. Experimental results show that the improved deep learning model can accurately and quickly segment blood vessels on mobile devices.
Towards an Understanding of Arab Women Researchers Contribution in Arabic NLP

Arabic language is spoken by around 400 million people living in the Middle East, North Africa, and the Horn of Africa. Like other languages, literary Arabic continues to evolve; this suggests a large potential audience for Arabic Natural language processing (NLP) and the need for a fair contribution from both men and women Arabic NLP researchers. Our paper will explore Arab women researchers’ contributions in the field of Arabic Natural Language processing research (ANLP) by answering 9 research questions. We answer these questions through examining research metadata from Digital Bibliography & Library Project (DBLP) and Google Scholar. We found that the proportion of Arab women researchers in ANLP compared to men is similar to the international level. We also observed their publication, collaboration and citation patterns. Our results point to the need for gender equity initiatives to increase the number of Arab female researchers in ANLP.
Analysis of English Machine Translation Standards of Professional Terms under Big Data Context Technology

In recent years, with the development of science and technology, information technology has also been widely used in various industries. In today's era of big data, great changes have taken place in translation, from the traditional manual translation at the beginning to the current machine translation. The advantage of machine translation lies in its fast translation speed and low cost, but certain errors will also occur in the translated translation. The translation errors of professional terms account for a larger proportion, which greatly affects the quality of the translation. At the same time, professional terminology is the core knowledge in the article, which will directly affect people's understanding of technical information in the professional field. The purpose of this article is to study the English machine translation standards of professional terms in the context of big data. This article focuses on the English machine translation of professional terms, and analyzes the English machine translation standards of professional terms in the context of big data based on relevant research at home and abroad. This article will analyze its professional terminology translation standards from all aspects, summarize and summarize the translation standards studied by various scholars, analyze the factors that affect translation standards, and discover various characteristics of translation standards. At the same time, in order to solve the shortcomings of machine translation in the translation of professional terminology in the past, this article attempts to summarize a set of objective and comprehensive English machine translation system evaluation standards for professional terminology, so as to improve its use efficiency. The experimental results show that from the ranking results, the highest comprehensive evaluation score is candidate translation 1, and the weight is as high as 27%. In the analysis of the translation results, information characteristics are added to reorder the candidate translations, and the most suitable context and the best translation are selected.
Potential Long-Term Consequences of the Coronavirus Crisis for the Transport Industry

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Parameter Analysis on Backprojection for Super-Resolution (SR) Reconstruction Algorithm

Abstract In this paper, an image SR reconstruction scheme by using k-Singular Value Decomposition (k-SVD) with Orthogonal Matching Pursuit (OMP) as sparse coding method was proposed to obtain the High-Resolution (HR) image. The system conducted in this paper consists of two parts: image SR reconstruction algorithm and also the backprojection process. Since this paper is focused on analysing the effect of parameters in backprojection on the performance of final images produced at the end of the process, therefore, the flow of backprojection is discussed. Generally, the backprojection algorithm is added to the approach to improve the image quality by sharpening the edges of the HR image. However, the parameters used in backprojection could be the factor that caused decrease in the image quality. Thus, the main idea of this paper is to analyse these parameters throughout the flow of backprojection algorithm. The parameters include the predefined 2D filter and interpolation method. Based on the results, it can be concluded that the use of averaging filter ( hsize = 3), Gaussian filter ( hsize = 5, 7 and 9; σ = 1) and bicubic interpolation method or also known as the cubic kernel can be adopted to the backprojection algorithm since these parameters achieved the highest RMSE, PSNR and SSIM values of 13.87, 25.29dB and 0.83 respectively. The analyses done in this paper has brought a clear understanding on the backprojection algorithm and further implementation and analysis in backprojection such as by using cascaded filters for averaging and Gaussian filters can be considered in the future.
Unit Traffic Classification by Two-stage Clustering on P2PTV Applications

Peer-to-peer video delivery (P2PTV) is still one of popular applications to enjoy video streaming service. To grasp and understand P2PTV traffic characteristics, a two-stage unit traffic clustering method has been proposed. In this paper, we verify the efficiency and generality of the proposed method through classifying mixed traffic data sets generated by two P2PTV applications, PPStream and PPTV. From the obtained results, we successfully obtained six components of unit traffic that are independent of each other.
Lower Bounds on the Expected Excess Risk Using Mutual Information

The expected excess risk of a learning algorithm is the average suboptimality of using the learning algorithm, relative to the optimal hypothesis in the hypothesis class. In this work, we lower bound the expected excess risk of a learning algorithm using the mutual information between the input and the noisy output of the learning algorithm. The setting we consider is, where the hypothesis class is the set of real numbers and the true risk function has a local strong convexity property. Our main results also lead to asymptotic lower bounds on the expected excess risk, which do not require the knowledge of the local strong convexity constants of the true risk function.
Information on Potential Vulnerabilities for New Requirements: Does It Help Writing Secure Code?

Recent research advocates a proactive approach toward addressing software vulnerability, i.e., identification and resolution of vulnerability before exploitation. To that end, a recent research has presented a framework to provide developers with information related to vulnerabilities that are identified with the existing implementation of functionally similar requirements. The idea is that a developer implementing a new requirement may learn from such vulnerability information and write her code in a secure manner. Given the various technologies and platforms a developer may use to implement the current system, to what extent such information would actually help in writing secure code is an open question. In this paper, we design a human subject study to explore how information related to potential vulnerabilities influence developers on secure implementation of new requirements. We further present a pilot run of our study with 50 participants. The results suggest that developers with limited professional experience could be a major beneficiary of the information on potential vulnerabilities.
Research on the Application of Blockchain Technology in the Collaborative Governance of Beijing-Tianjin-Hebei Ecological Environment

The problems of ecological environment restrict the high-quality integrated development of Beijing-Tianjin-Hebei region. At present, blockchain is setting off an upsurge of innovation in the world, and China has also proposed the development strategy of blockchain power. Under this background, the construction of Beijing-Tianjin-Hebei ecological environment collaborative governance mechanism based on blockchain technology is of great significance to create a good environment for blockchain innovation, promote the high-quality development of the real economy and promote the sustainable development of the real economy It is of great significance to promote the coordinated development of regional economy. The government should give full play to its guiding role in technological innovation, make overall planning and reasonable layout for the collaborative governance mechanism of Beijing Tianjin Hebei ecological environment based on blockchain technology, start from the path of environmental protection and innovation drive, realize the construction of the collaborative governance mechanism of Beijing Tianjin Hebei ecological environment based on blockchain innovation, and improve the contribution of the collaborative development of Beijing-Tianjin-Hebei ecological environment.
The High Achievers’ Learning Strategies And Experiences In Learning English

Abs trak. This study aims to explore the language learning strategy and language learning experience by the high achiever students in the English Department of UNM. The study employed interview, observation, and questionnaire using blended technique, online and offline. There are 3 respondents from English education students contribute to this study. The results suggest that the students used direct and indirect strategies interchangeably with emphasized on the indirect one. All respondents are exposed to English since early ages. Since then, they actively involved in English competition and English society. K ata Kunci : Learning Strategy, Learning Experience, High Achiever Students
Particular solutions of equations with multiple characteristics expressed through hypergeometric functions

Abstract In the paper, similarity solutions are constructed for a model equation with multiple characteristics of an arbitrary integer order. It is shown that the structure of similarity solutions depends on the mutual simplicity of the orders of derivatives with respect to the variable x and y , respectively. Frequent cases are considered in which they are shown as fundamental solutions of well-known equations, expressed in a linear way through the constructed similarity solutions.
Determining the Legibility of Fonts Displayed in Augmented Reality Apps for Senior Citizens

Over the years, continuous innovations in computer vision, as well as human-machine interaction, have renewed interest in augmented reality (AR) technologies as a promising and prominent means of interaction and visualization. Elders, as a social group, can benefit from this technology, especially in their reading skills. For our study, we sought to evaluate, compare, and determine the typeface and font size that had greater legibility for older adults, using augmented reality technology for this purpose. Physical reading tests were applied to elderly users, using various fonts and sizes, to determine their needs and experiences based on the results. There was the participation of 12 volunteers and the fonts Arial, Times New Roman, Georgia and Verdana were used, which were presented in sizes 8, 10, 12 and 14 respectively. The results showed that typography does not have a significant effect related to reading time using augmented reality (p-value = 0.456), but the font size (p-value = 0.001) and the gender of the participant (p-value = 0.037) do, because the smaller size generated more delay. The qualitative analysis indicated that Verdana and size 14 were the preferred typeface and font size for the sample.
Fast detection method of urban road asphalt pavement crack based on image recognition

Based on the previous research results, the application of image recognition technology to the rapid detection of urban road asphalt pavement cracks, a fast detection method of urban road asphalt pavement cracks based on image recognition is proposed. This paper designs an image acquisition device of pavement cracks to collect the images of pavement cracks. The road image is denoised and enhanced. Based on image recognition, fracture classification is carried out. Firstly, boundary tracking is carried out, then small area image processing is carried out, and finally fracture classification is carried out. Different methods are used to measure and calculate the relevant parameters of cracks, to realize the rapid detection of cracks. After testing, the design method can improve the overall visual effect of the image to a certain extent, and achieve a higher crack detection rate, which has a broad application prospect.