Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review of Cloud based Multi-Authority Revocable CPABE Schemes

Security concerns related to the cloud service model cannot be efficiently addressed using conventional cryptographic techniques. Therefore, Ciphertext Policy Attribute Based Encryption (CPABE) became the suitable choice for the data owners to enforce fine-grained access control. Even though the sensitive data resides with Cloud Service Provider (CSP), however, the data owner has complete control to determine who can access the data. CPABE schemes in existing work have been implemented either using single-authority or multi-authority systems. In this work, multi-authority pairing-based revocable CPABE schemes methodology has been thoroughly studied. The methodology adopted by the existing schemes to revoke malicious users has been analytically compared using various parameters. In addition, the approaches addressing change in users’ roles, privileges, or attributes have also been critically reviewed. Subsequently, the challenges and research gaps identified in the existing schemes have been outlined. Conclusively, in this study, the properties required in an efficient multi-authority pairing-based revocable CPABE framework have been presented as a possible solution to subdue the identified research gaps.
Data Security Management Based on Transparent Encryption Policy

With the advent of the "data security management system", the management of portable media devices has been balanced and standardized to prevent the leakage of internal sensitive information and ensure the security of internal sensitive information. However, if the "data security management system" itself does not use effective technology to protect security, it may be compiled, analyzed, cracked or attacked by hackers, thereby losing data security capabilities. This paper aims to study data security management based on transparent encryption strategy. This paper first briefly introduces the research status of related technologies at home and abroad and the practical significance of the subject. A detailed overview of transparent encryption technology is given. The difference between two different transparent encryption techniques is explained. This paper builds a new data security management system based on the above technology, and conducts many experiments on the system to test the security and confidentiality of the system. Finally, an outlook on the future development of data security management is given. Experiments have shown that when the amount of data is small, the transparent encryption strategy is slightly slower than the traditional conventional encryption strategy, but with the increase of the data amount, the speed of the transparent encryption strategy will exceed that of the conventional encryption. data is less than half the time of conventional encryption. After transparent encryption policy data, it can resist network attacks for more than 1 hour in data security management.
Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment

Cloud computing (CC) delivers on-demand and powerful business environments. It can be constructed on top of virtualized data hubs. Virtualization renders a flexible infrastructure for cloud. However, scheduling the tasks and managing the resources in virtualized data hubs will be difficult. Maximizing the virtual machine utilization and minimizing the total makespan is imperative in the cloud environment. The task scheduling issue can be indicated as multi-objective optimization problem, which is solved by utilizing optimization methods. This study develops a Mantaray Foraging Optimization based Makespan Enhancement in Cloud based Scheduling Environment (MROME-CSE) technique. The presented MROME-CSE technique aims to allocate the resources proficiently in the cloud environment. In the presented MROME-CSE technique, the behavior of manta rays is mainly involved. Furthermore, the MROME-CSE method derived an objective function to allocate n individual tasks to m heterogeneous accessible resources, thereby task conclusion period was minimalized and resource was fully exploited. The experimental evaluation of the MROME-CSE technique is examined briefly. The experimental outcomes stated the superior performance of the MROME-CSE technique over other recent models.
Application of K-subgraph Algorithm in Network Privacy Protection

In view of the social network privacy protection issues, a new privacy protection method - k - subgraph division rules is proposed through the social network segmentation and generalization vertex labels. The figure structure characteristics are disrupted to anonymize the social network and achieve the purpose of privacy protection. Simulation experiments show that this method offers a network with individual privacy protection, and availability is guaranteed for an anonymous social network at the same time.
Towards an approach for online evaluation of new variants of content-based POI recommender systems by mobile tourists

Recommendation systems (RS) play an important role in our everyday life, especially in domains such as e-commerce, online advertising and tourism. Indeed, these RSs provide suggestions that are closer to user expectations, thanks to algorithms inspired by user previous behavior. In the literature, content-based RS are used for the recommendation of movies, articles, books, etc. These RS are based on the annotations (tags) of items to be recommended on the one hand and on the declaration or inference of user profiles on the other hand. In the tourism domain, this type of approach requires adaptations to integrate the spatiotemporal dimensions of POIs (Point Of Interest) in the process of recommending places to visit. Moreover, most of the existing works in the literature only use similarities between POIs to make suggestions to tourists and don’t calculate the prediction of future ratings given by tourists for the recommended POIs not yet visited. To overcome these problems, we propose three new content-based recommendation approaches for the tourism domain, the first one makes non-personalized recommendations using POI/POI similarity and the other two approaches make personalized recommendations based on inference of tourist profiles. In our work, we describe tourist profiles and POIs profiles using a list of tags defined by tourism domain experts. Then, the browsing history (ratings, comments) of mobile tourists is collected progressively from our website of tourist places of Chlef department for providing an online evaluation of our three approaches. The results of our experimentation allow us to compare our three approaches using the acceptance rate of POI suggestions by real users and show which approach mitigates the most the problems related to cold start and data sparsity.
Construction Project Management System Based on Improved Artificial Bee Colony Algorithm

Due to the long engineering cycle of construction projects, the large number of project participants, and the huge amount of project information, the project management of construction enterprises has put forward high requirements such as high efficiency and strong pertinence, and construction enterprises have to find an efficient management method for modern construction project management. In this regard, this paper combines ASP NET and VB NET development tools to design a construction project management system based on B/S mode, and uses an improved artificial bee colony algorithm to optimize the system. The number of bug occurrences plays an important role.
Recommendations for Developing a Sign Language Recognition Application for Malaysia

Deaf and mute people face significant challenges in carrying out daily tasks that can cause them to be ignored by society. They mainly have issues in communicating with the people without disabilities, who represent the majority. Thus, several sign language applications have been developed aiming to help them in communication. Still, most of those applications are unsuitable for implementation in Malaysia because every country has its own sign language. In Malaysia, there are limited MSL (Malaysian Sign Language) applications and vocabularies that are also not very efficient in strengthening communication between deaf and mute people with individuals without disabilities in real time. In this research, the authors have explored the previous studies and existing applications in detail. In addition, they have carried out interviews and questionnaires to gain more in-depth knowledge related to this topic. Finally, by utilising the acquired knowledge, the authors have proposed a solution to improve the communication between people without disabilities with deaf and mute people with the help of a mobile application-based solution that uses smart sign language recognition.
A Methodology for Crop Price Prediction Using Machine Learning

Every nation on the globe depends heavily on agriculture. Numerous issues with agriculture still need to be resolved, such as yield recommendations and crop price forecasting based on market value. This might boost farmers’ livelihoods and increase yield as well. Modern technologies of today have also been demonstrated to be capable of producing an automated advanced application for a diverse range of purposes. Machine learning models namely, decision tree and neuro-evolutionary algorithms are used in the proposed work to provide an improved crop productivity and pricing estimate. The paper provides a comprehensive analysis on the machine learning based techniques/algorithms used in the agricultural segment. The outcomes of the proposed system were demonstrated and illustrated how far it is efficient. As an extension to this work, an automated price recommendation system using genetic algorithm shall be designed in the future.
Inclusive Financial Information System Based on Big Data

Inclusive finance has strongly promoted the development of the economy, and more importantly, it has enhanced social fairness and public sense of gain, which has played an important role in the smooth operation of society. The purpose of this paper is to study the design and implementation of inclusive financial information system based on big data. Introduces the relevant technologies that need to be used in the design and implementation of the system, and lays a theoretical foundation for the design. Starting from the functional requirements. Determine what kind of system should be developed, and clarify the goals and behavior of the system. The whole system is described through three aspects: overall architecture design, system function structure design and network structure design. By dividing the system modules, clarifying the system structure, establishing a reasonable system structure system, and managing the background management modules of the system in the form of data analysis page display detailed design and implementation.
Solfeggio Training System Based on CAT Technology

Solfeggio teaching is likely to experience a major transition from tradition to modernity and from manpower to computer in the future. However, in fact, this transformation has begun at the moment, and one of the most prominent manifestations is the emergence of various solfeggio teaching software. The purpose of this paper is to study the solfeggio training system based on CAT technology. This paper analyzes the current situation of traditional solfeggio teaching and the current research and development of digital solfeggio teaching software at home and abroad, introduces related concepts of solfeggio music theory, and deeply analyzes the digital audio related to the solfeggio system based on CAT technology. technology, MIDI technology, pitch detection technology. On this basis, the overall frame design and core module implementation of the solfeggio training aid system are completed. At the same time, the prototype system and the system operation effect are investigated. Practice has proved that the use of CAT technology in the teaching of solfeggio enriches the teaching content.
Software Defect Prediction Technology Based on Fuzzy Support Vector Machine

Software defect prediction(DP) is one of the most active research areas in software engineering. DP models can provide error-prone source code components or changes, etc., so that quality assurance teams can deal with error-prone source code with more effort, effectively allocating limited resources to verify software products. As software projects grow in size, DP techniques will play a vital role in the work of developers, helping them to develop more reliable software products, resulting in faster time to market. In this paper, fuzzy support vector machine (FSVM) is introduced to build a software DP model, which is trained on the data set by the FSVM classification module. In this paper, by comparing the mean square error of the training data of SVM and FSVM, in the subset of low-defect modules, the training result of FSVM is not as good as that of SVM, that is, the prediction accuracy of SVM is higher; while in the subset of modules with high number of defects, the training result of FSVM is better than that of SVM, that is, the prediction accuracy of FSVM is higher.
Wushu Sports Target Tracking Algorithm Based on BP Neural Network

Object detection is to distinguish and extract moving objects or changing regions in a video from the background. In recent years, great progress has been made in the detection research based on moving objects, the algorithm has been further optimized, and the application scope has become more and more extensive. The purpose of this paper is to study the Wushu target tracking algorithm based on BP neural network. In this algorithm, object tracking is considered as an online transfer learning process. Move to electronic target tracking. In addition, during the offline training process, the network model learns the similarity between different target frames in the video sequence, so that the network weights do not need to be updated online, which greatly improves the real-time performance of the tracker. Finally, the average accuracy, average hit rate, and frame rate of the algorithm in this work are compared with MeanShift, CT, Struck, and TLD in the dataset. The results show that the average accuracy rate of the algorithm in this paper is 85.4%, and the average success rate is 73.4%, both of which are at a high level. For the other four algorithms, the frame rate reached 27.384 fps, which is a good result.
Using new bit-reorganisation and non-linear function layers to enhance the LTE ZUC algorithm

Due to the development of science, technology, and society, and with the advancement of life quality, more interest has been given to security issues. There are big challenges in mobile communications that should be overcome for an advanced level of security against new threats and risks. LTE has developed and grown into one of the powerful technologies that fulfil the performance goals of 4G mobile communication. ZUC stream cipher algorithm is the core of confidentiality and integrity LTE algorithms and has been extensively applied to 4G mobile communication security. ZUC algorithm was developed as a powerful alternative to different algorithms that were common before. In this paper, a modified version of the ZUC algorithm is proposed to reduce the encryption time and improve LTE security. MATLAB® R2019a has been used for the implementation. The results elucidated that the encryption time is reduced by approximately 4.25 % using the proposed algorithm. The proposed algorithm is also used to encrypt a sample text message where 1.198472 seconds is required for encryption compared to the original ZUC algorithm which required 1.458467 seconds.
Intelligent Document Finding using Optical Character Recognition and Tagging

In the era of digitalization, the assortment and exploration of great volumes of documents is becoming progressively significant for enterprises to increase their productions and practices. Optical Character Recognition (OCR) is a procedure of identifying text in scanned (image-based) documents. This paper aims to deliver seamless searching of documents in file systems using Optical Character Recognition (OCR) and Natural Language Processing (NLP). Our paper includes the following phases: "Text Identification (in terms of text files), Image Capturing, Image Enhancement, Image Identification, OCR, Data Extraction and Quality Assurance". In case of text files, the data extraction is done in the first phase itself. The document management system "processes both structured document images (ones which have a standard format) and unstructured document images" (ones which do not have a standard format). In the tagging phase, the document is divided into segments and the tags for each segment are generated using Natural Language Processing.
A Framework for Handwritten Date Recognition in Quality Documents

Document text recognition is an important task in optical character recognition. Due to the difficulty in collecting real handwriting samples and the variety of handwritten characters, there are still challenges to recognize handwritten text in quality documents. In this paper, we propose a framework for handwritten date recognition in nuclear power quality documents and a pre-training data construction method to improve the recognition effect. This framework specifically consists of an image feature extraction module and a text transcription module. The text transcription module combines the text transcription functions in Convolutional Recurrent Neural Network (CRNN) and Show-Attend-and-Read (SAR). The experiments are evaluated on handwritten date recognition with nuclear power quality documents. The evaluation presents the framework achieves obviously improvement on metrics and the pre-training dataset can help speed up training.
An Application System for Evaluating and Optimizing the Quality of Neural Machine Translation Corpus

With the rapid development of e-business, cross-border trade is becoming more and more prosperous. Facing different language speakers, the use of machine translation will provide a great convenience for communication. To build a machine translation system, in addition to designing an excellent model, it also needs to have a large amount of corpus data for training. The quality of corpus will greatly determine the translation performance. Therefore, it is an important task to evaluate and optimize the corpus quality. In this paper, we design three evaluation and optimization algorithms for the quality of neural machine translation (NMT) corpus. On this basis, we integrate these algorithms and design an application system.
Gender Detection using Facial Features with Support Vector Machine

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In Image Processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.
Design and Implementation of web application penetration testing with Cross-site scripting (XSS)

In our daily lives, no single days we use Web Applications such as Gmail and YouTube. Without using Web Application, people cannot do their jobs and pay in restaurants. These applications are designed by various technologies like JavaScript, HTML, PHP and so on. However, as no system is perfect, Web Application includes vulnerabilities at the design level as well, which leads to security breaches and to steal users’ sensitive information. One of the famous Web Application vulnerabilities is Cross-site scripting (XSS). This paper discusses the implication of XSS, in a real case scenario to demonstrate how it deployed by hackers. This is followed by a discussion on awareness and mitigation technique to prevent users from becoming a victim of XSS attack on web applications.
Feature Distribution-based Knowledge Distillation for Deep Neural Networks

In recent years. various compression methods and compact models have been actively proposed to solve the significant computational costs accompanied by the achievement of high accuracy in deep learning. In particular, the knowledge distillation (KD) technique, which achieves a significant network compression effect by using the information of large teacher networks for the training of small student networks, is receiving a lot of attention because it has high scalability and reusability compared to the development of a compact model with a new structure. In this paper, we propose feature distribution-based knowledge distillation (FDKD) that effectively transfers semantic information using only the distribution information of feature maps obtained by a simple operation. Experimental results show that the proposed method improves the accuracy by up to 5.26% and 1.38%, respectively, compared to the baseline (i.e., Vanilla) and the existing KD scheme.
Identification of Motion and Prediction of Time Series for Mobile Robot Sensors based ANFIS and Neural Networks Using LSTM

Without employing a mathematical model, combination neural networks (NNs) and fuzzy logic control have been identified as the primary techniques for enhancing the performance of coupled nonlinear robot systems. Two-wheeled mobile robot system kinematic model identification algorithm is presented in this paper using Adaptive Network Based Fuzzy Inference System (ANFIS) and NNs. Depend on a Modified Elman recurrent NN (MERNN) with a back propagation algorithm, the structure of the dynamic NNs will be analyzed that use to learn the neural modeling approach. The performance of the system can be enhanced by first training a NNs model offline, and then transferring that model into a process in which adaptive learning is carried out using ANFIS. According to the simulation findings, the suggested modelling can provide superior control performance in terms of reduced tracking error and precision in tracking trajectories between the mobile robot’s intended output and its actual actions (orientation and position) and the model of output neural. Furthermore, a set of rules for a fuzzy controller can be constructed without in-depth information about the system. A precise model is not necessary when using the ANFIS control design approach. This paper also make a comparison between LSTM, phased LSTM, and stacked LSTM architectures for time series prediction. The experimental data is motion sensor data collected during actual drives by a mobile robot. The LSTM stacking model superior the other two models, although all three models offer excellent predictions for the near future. Lastly, the actual and predicted trajectories of the robot are compared.
The Application of E-Learning in Chinese Education and Teaching

The continuous development of information technology has promoted the change of education and teaching methods One of the six major changes in the international promotion of Chinese is "the transformation of teaching methods from face-to-face teaching with paper textbooks to making full use of modern information technology and multimedia network teaching". In the past, Chinese education was mainly based on traditional methods. Chinese teaching is based on the traditional way of thinking. However, with the development of information technology, new teaching forms have emerged in China. For example, online courses for students to learn Chinese or culture have become very popular recently. In addition, some foreign teachers teach English in China on the condition that they can use their own methods and techniques (for example, using Skype).