Reviewerly/exHarmony huggingface dataset:
The Application of Computer Vision Synthesis Technology in the Editing of Film and TV Shooting

Post-compositing technology is an important final stage in the film or TV production process. The post-construction technology is to first compose the film according to the camera table, and then combine the previously produced video and image elements through the software. This paper studies the application of computer vision synthesis technology in the post-production editing of FAT (FAT). Based on the literature, we understand the post-production editing of FAT, and then investigate the application of computer vision synthesis technology in the post-production editing of FAT. The result is that computer vision synthesis technology in FAT can enrich the level of the FAT and the form of visual presentation is more colorful.
E-restaurant management system based on Laravel framework

A short time ago, food manufactures have been growing quickly due to the evolution of new technology. Numerous studies has been conducted to develop products to achieve the needs of customers. In this research we used Laravel as framework to make Electronic Restaurant Management System (ERMS) because Laravel is a robust PHP framework, prepared for developers who lack a simple and amazing toolkit to produce perfect featured web applications. The executives body could be utilized the request for handling the eatery administrations carefully, starting from generation of sustenance components for the multi touchable smart eat list to deleting it or to direct requests from customers appropriate to announce it. The framework was constructed utilizing PHP scripting and MySQL database over Laravel System. This research displays the operation of the web based ERMS with goal to improve the dining table support. In the eatery segment, web authorize PC was proposed to deal with the traffic stream of the requests, producing appropriate charging reports, decrease client waiting time, increment the productivity of work process and to decrease inaccuracy notes.
Securing speech transmission based on DNA and S-Box techniques

Speech signal scrambling and encryption are important multimedia technologies. These technologies are required in the field of speech to satisfy the security and privacy of information requirements for transmitting a large audio signal across a connection. In this article, we presented a two-step safe cryptographic method for speech transmission: a suggested scrambling algorithm is suggested to verify that the actual speech signal is changed as a result of a suggested speech signal transferring algorithm, an encryption scheme for the scrambled speech signal is suggested based on the coding of DNA and a sequence of keys production by a suggested Substitution box using 256 keys based on the quadratic map, logistic map, and Lorenz map that proves difficult to recover by any attack.
Fuzzy logic-based self-adaptive artificial bee colony algorithm

We are presenting a dynamic technique for improving the basic artificial bee colony (ABC) algorithm performance using a fuzzy logic system termed DABCFL. It consists of an intelligent self-adaptive technique based on the expertise of humans to establish a fuzzy logic system to increase efficiency of the exploration and exploitation of the area by increasing or decreasing the size of population across cycles. This is done based on the values of three different population diversity measurements. Experiments are achieved on 20 widely used benchmark functions. The findings reveal that the DABCFL algorithm beats the basic ABC algorithm in all tests.
Automatic Transformation Search Against Deep Leakage From Gradients

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we systematically analyze existing reconstruction attacks and propose to leverage data augmentation to defeat these attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract training samples from the corresponding gradients. We first design two new metrics to quantify the impacts of transformations on data privacy and model usability. With the two metrics, we design a novel search method to automatically discover qualified policies from a given data augmentation library. Our defense method can be further combined with existing collaborative training systems without modifying the training protocols. We conduct comprehensive experiments on various system settings. Evaluation results demonstrate that the policies discovered by our method can defeat state-of-the-art reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
Design of Intelligent Recognition Corpus System Based on Ternary N-gram Algorithm

With the rapid development of computers and the Internet, mankind has entered a new information age. Language processing plays an irreplaceable role in the information age. The emergence of language processing research mainly solves the problem that computers cannot understand human language. However, there are still deficiencies in the field of intelligent recognition language today, and it is very necessary to further optimize the intelligent recognition corpus. Therefore, based on the ternary N-gram algorithm, this paper studies and designs an intelligent recognition corpus system. First, this article explains the composition of the corpus and the concept of the intelligent recognition corpus; then, the application of the ternary N-gram algorithm is studied, and on this basis, the framework and modules of the intelligent recognition corpus system are designed. Finally, the system is tested, and the test results show that the accuracy of the ternary N-gram algorithm is higher than that of the traditional algorithm, and the response speed of the optimized system is faster than that of the traditional algorithm.
Medical image compression using IIR wavelet filter banks

The proper utilization of digital medical imaging technology and applications requires image compression. The proposal in this paper is based on the IIR wavelet filter bank, and it suggests an image compression algorithm. The performance of the scheme has been measured using prescribed performance measures. The results demonstrate that the proposed method performs preferable compression than other compression methods. The results obtained are compared with wavelet-based filters bank in order to improve the compression using our approach. When compared to standard approaches, the results are quite excellent in terms of compression ratio and compressed image quality.
An Effective Method for Mining Negative Sequential Patterns From Data Streams

Traditional negative sequential patterns(NSPs) mining algorithms are used to mine static dataset which are stored in equipment and can be scanned many times.Nowadays, with the development of technology, many applications produce a large amount of data at a very high speed, which is called as data stream.Unlike static data, data stream is transient and can usually be read only once.So, traditional NSP mining algorithm cannot be directly applied to data stream.Briefly, the key reasons are: (1) inefficient negative sequential candidates generation method, (2) one-time mining, (3) lack of real-time processing.To solve this problem, this paper proposed a new algorithm mining NSP from data stream, called nsp-DS.First, we present a method to generate positive and negative sequential candidates simultaneously, and a new negative containment definition.Second, we use a sliding window to store sample data in current time.The continuous mining of entire data stream is realized through the continuous replacement of old and new data.Finally, a prefix tree structure is introduced to store sequential patterns.Whenever the user requests, it traverses the prefix tree to output sequential patterns.The experimental results show that nsp-DS may discover NSPs from data streams.
Analysis of password recovery schemes: Review

People's memories aren't perfect; they continuously memorize new information while forgetting old ones. For example, losing a password is a common issue that IT help desks encounter. The Proposed system is present many protocols that allow a user to recover a password automatically using only a portion of the password. The recovery of a password is just as important as the login process, as both can be hacked. A non-legitimate user can be able to gain access to an account by using password recovery techniques. Rather than allowing original passwords to be recovered, There are many techniques used to recover forgotten passwords by using several methods such as links that allow password reset via email and password verification questions. People memorize new facts all the time, but they often forget old ones. All of these systems have the same goal: Some security solutions can be provided to legitimate users to enable them to recover a forgotten password, as well as obtain a new password, which means allowing the old password to be reset. This paper presents techniques used by researchers in this field and recommends the most important methods.
Pengaruh Model CIRC terhadap Kemampuan Menulis Karangan Narasi Kelas IV SD

This study aims to find out whether there is an influence on the use of the Cooperative Integrated Reading and Composition (CIRC) learning model on the ability to read students' narrative essays. This research uses a descriptive type of research, where the source of the data obtained is from discussion activities during the learning of grade IV students of SD Berangah. The instrument used in this study was to use an observation sheet. The analysis technique used to determine the reading comprehension of students' narrative essays is the technique of skin data analysis techniques. The results showed that there was an influence of the Cooperative Integrated Reading and Composition (CIRC) type cooperative learning model on the ability to read narrative essays of grade IV students of SD Berangah. This can be seen from the ability to read narrative essays of grade IV students both after being implemented by the CIRC learning model.&#x0D; Keywords: CIRC Model, Writing Ability, Essay, Narrative.
Medical image enhancement using histogram equalization techniques

Due to the rapid development in digital technology, image enhancement has become a necessity to extract data and use it in many fields that may be medical, agricultural security, and many other fields. There are many Image enhancement technique used in image processing. The proposed method have been presented in this paper attempt to improve the performance of histogram equalization with different kind of medical image by using Gaussian filter and gamma where combined together to improve the illumination, contrast of images ,reduce the noise, and also improve image quality coefficients. Try to measure the entropy of image and compare the result of output image with input image and to give desired result. The quality coefficients for the medical images processed with the HE and Gaussian filter and Gamma with certain value, according to the scientific proof. When the image quality was improved, the visual perception was improved.
The tensor product bipartite graph for symmetric encryption scheme

This work presents new contribution of graph theory for symmetric cryptographic applications. A tensor product bipartite graph (TPBG) is used to propose new symmetric encryption schemes. On proposed encryption schemes, the ciphertexts of the plaintexts are sent to receiver entity as the TPBGs. Based on two graphs G1 and G2, the TPBG is created depending on the random choices of the plaintext bits. These bits are used as vertices of graphs G1 and G2. The purpose for using the TPBG is to increase the security level of the symmetric encryption schemes in compare with other encryption schemes. Two study cases of the proposed symmetric TPB graphic (STPBG) encryption schemes are presented as new experimental results. The security issues of the proposed STPBG encryption schemes are determined. The STPBG encryption schemes consider as new insights for more secure communications.
Pattern Recognition Based on Variant Reinforcement Meta-learning

Pattern recognition is the study of automatic processing and interpretation of patterns using computers through mathematical techniques, where the environment and the objects are collectively referred to as “patterns”. It studies the problem of classifying samples by their attributes using computational methods. The main research directions of pattern recognition are computer vision, image processing, and natural language processing. In this paper, we discuss the application of a variant of reinforcement meta-learning, which combines experience replay and meta-learning in reinforcement learning, to the problem of continuous learning in pattern recognition. It also compares its accuracy with current solutions such as online learning for the continuous learning problem in pattern recognition. It mainly consists of reinforcement learning, reptilian meta-learning and experience replay in SGDM optimizer. It is finally concluded that for the problem of continuous learning in pattern recognition, its ability to preserve past learned knowledge with rapid learning of current knowledge is higher than the current online learning. Based on this, the effect of different parameters on the performance of this variant of reinforcement meta-learning is explored.
Analisis Terhadap Minat Membaca Siswa Kelas Tinggi di Sekolah Dasar

This research has done at Sekolah Dasar Negeri 1 Pringsurat. The aims of the research are to know how is student reading interest, the efforts for improving student reading interest, to find out supported factors ad unsupported factors on students’ reading interest. The method of the research is Mix Method. The kind of the research is Survey research. The subjects and the populations of he research are high grade students (IV, V and VI) and it is consist of 60 the object of the research is students’ reading interest. The technique of the research are: indirect communication, direct communication, direct obsevation and study documentation. The instruments of collecting data are questionnaire, interview, observation and documentation. After analyzing of the thesis, the result shows that percentage students’ reading interst is 80%, furthermore, it is known that supporting factors of student’s interest of reading are student awareness display tools, methodes used, good situation and supporting environtmen. Mean while the obstacles are the teacher does not know what student’s want is, lack of facilities and the teacher does not master the methode that appropriate with student’s want, moreover to improve student’s interest of reading it needs some efforts like develop student’s happiness toward the passage so that obligation but, because reading is their hobby and give the understanding to student’s that reading is important. It can said that tudents’ reading interest at S ekolah Dasar Negeri 1 Pringsurat are good and very strong.&#x0D; Keywords: Students’ Reading Interest
License Plate Recognition System Based on Neural Network

Artificial neural network has excellent data fitting ability and is widely used to predict the development trend of complex nonlinear systems, However, the research on the application of artificial neural network in intelligent traffic regulation has the problems of difficult access to traffic sample data caused by insufficient development in the field of traffic monitoring in China, and it is impossible to verify the effectiveness of artificial neural network in the regulation of regional traffic system only by predicting the traffic flow of a single road. Intelligent transportation system applies advanced information technology, computer technology, sensor technology and image processing technology to realize the automatic management of vehicles. As an important part of intelligent transportation system, license plate recognition plays a vital role in traffic management. Therefore, the research on license plate recognition technology has very important practical significance.
Image encryption using RC4 based 5D-chaotic system under spatial domain

Information security is the most modern filed today and with the rapid advancement of communication technology, the high speed of electronic data exchange, and the massive volume of data, information security, data storage and transmission, and transmission, as well as, the widespread use of image in industrial process, has become a critical issue to protect, and coding systems have played an important role and vital working the field of modern cryptography. Images are encoded utilizing the size of the original image to generate the key using a five-dimensional chaotic system in this work. This signifies a big key area, and color images are encoded using the RC4 technique. The proposed method has proven its ability to resist statistical attack because the graph of the encoded images is completely uniform and the correlation analysis values are close to zero because this algorithm has the best way to defend the image content and resist brute force attacks and other attacks. This procedure makes the image's content more secure.
Quantum Network Architecture and Its Topology

In this chapter, the idea of the deployment of quantum key distribution (QKD) techniques within a more realistic insecure network has been discussed for achieving unconditional secure communication between two or more legitimate users in presence of eavesdroppers. The architecture of the network can play a crucial role in protocol design with trusted or un-trusted repeaters/relays in between these legitimate users. Point-to-point link (P2P) communication for long distance-based communication is not desirable as per the security concern. The present QKD techniques can achieve low-distance group communications where one trusted server can authenticate the users within the group. Stand-alone QKD communication architecture can be extended to create a wide network using the relays at regular intervals depending on the network topology. Efficient QKD protocols with proper network topologies have been discussed for achieving better security in different eavesdropping scenarios.
Datasets classification using deep learning and machine learning classification algorithms

The process of building new dataset and the existence of such a data followed the urgent need for the existence of datasets that are specialized in educational lectures, so this will need an accurate classification algorithm to classify it, the benefit of classify such dataset is to minimize the workload of classifying each file manually and individually. In the present paper, authors perform experimentations for conducting an empirical deep learning study, especially, convolutional neural network, for three new datasets of educational lectures which are (PDF, Word and PowerPoint datasets), The three new datasets using real data educational resources lectures collected from various document projects of different universities and institutions. The architecture has been applied to the task of the text classification in the domain of the document with documents data-sets have been obtained from a variety of projects on actual document cases. The aim of the present study is to initially test the performance of each dataset (PDF, Word, and PowerPoint dataset) through using four machine learning classification algorithms which are (Bayes Net, Random Forest, Random Committee, and OneR). Second goal is experimenting the efficiency of the approach of the deep learning in the tasks of classification and after that, comparing the efficiencies with the efficiencies of traditional machine learning classification methods. Mainly two classification techniques used to maximize the benefits of the classification process, the first one is to use the deep learning algorithm which shows an accuracy of classifying file between (95 and 96%) for three new dataset files and standard machine learning algorithms (OneR, Random forest, Bayes net, and Random Committee ) these algorithm shows accuracy 91% for PDF Dataset using random forest and random committee algorithms, for Word dataset the accuracy is 46% using random committee, and for the last dataset PowerPoint the accuracy is 77% using random forest, Therefore, we will choose Deep learning algorithm because it gives higher results and accuracy than machine learning algorithms.
Real-time speech recognition of arabic language

The development of an Arabic Automatic Speech Recognition system has recently attracted the interest of a number of researchers. Several attempts to build an Arabic Speech Recognition System were noted, and they yielded encouraging results. Most of these attempts, however, were limited to a reduced vocabulary. Our work falls within the general framework of the Automatic Speech Recognition; it treats the Arabic language using the Open-Source Java Speech libraries based on Hidden Markov Model HMM. We present in this work, the bases of construction of a system of automatic recognition of classical Arabic. A Speech-to-Speech android mobile application is design to translate the speech from Arabic language to English and vice versa.
Design of Decoration Energy Saving Integrated System Based on Genetic Algorithm and BIM

With the improvement of human aesthetic art and living technology, a variety of interior decoration has been produced. While meeting human artistic aesthetics, building interior decoration also poses a major challenge to its energy-saving effect. Taking the low-temperature interior decoration of the experimental adjustable room as an example, this paper analyzes, explores and optimizes the characteristics, structure and working mode of the experimental adjustable room temperature decorative board by using theoretical data analysis, practical detection, genetic algorithm and BIM Technology. Using genetic algorithm, the change law of temperature between the surface of adjustable room temperature decorative board and the indoor temperature of the building with time is obtained. The indoor air temperature and power consumption after the unit works at different times are measured. The results show that the surface air quality can meet the temperature requirements. The adjustable room temperature decorative panel can fully meet the needs of temperature regulation in summer in the tested area, and is evenly distributed in the vertical direction of indoor temperature. The results show that the application of BIM Technology is helpful to the design of decorative energy-saving integrated system, and genetic algorithm also improves the efficiency of accurate data analysis.
Cryptocurrency: Is it the Future of Payments?

Cryptocurrency, which is built using blockchain technology, is one of the newest methods of doing secure transactions without a central authority. Due to the unregulated means in which cryptocurrency is transferred, it becomes incredibly hard to ensure the fairness and legitimacy of the transactions. This is to further explore the history, process, problems, competition, advantages, and the current future of cryptocurrency. Finally, the team built its cryptocurrency and blockchain similar in functionality to Bitcoin, which is currently the most popular cryptocurrency by sheer market capitalization of over $898 Billion. This allows people to track the flow of cryptocurrency in all its flaws, and its potential succession of other currencies. This chapter explore different aspects of the cryptocurrency which is a vital aspect of futuristic cyber smart cities. This chapter will serve as a reference for cyber smart city developers and other relevant stakeholders in designing futuristic cities.