Reviewerly/exHarmony huggingface dataset:
Data Security Encryption Method Based on Improved AES Algorithm

There is a certain correlation between the keys in the process of e-commerce transaction data encryption, and due to the large data, the encryption time is too long, leaving serious security risks. In order to solve this problem, this paper designs a secure encryption method of electronic commerce transaction data based on improved AES algorithm. Firstly, the e-commerce transaction mode is analyzed, and multiple private chains are established to realize transaction processing. In the database, each enterprise uses its own certificate to generate the key of AES encryption algorithm. Affine transformation is used to transform the affine S-box of AES encryption algorithm, and use pseudo-random number key stream to realize data encryption and decryption. So far, the design of secure encryption method for e-commerce transaction data has been completed. The experimental results show that this method can shorten the data encryption time and has certain application value.
On-Orbit Health Management of Thermal Control Subsystem of a Micro Satellite

As a subsystem of spacecraft, thermal control plays a basic role in guarantee. According to the reliability requirements of a micro satellite, a thermal on-orbit health management system is proposed, which has ensured the normal operation of the thermal control subsystem of the satellite for more than one year and has an obviously reliability supporting effect. This paper introduces the relationship between the health management system and the thermal control subsystem at all levels, expounds and summarizes the three main parts of the management according to the actual on-orbit situations, and prospects a future advanced thermal management system. This management method can be used as a reference for reliability design and project development of other micro satellites.
A Clustering Fault Diagnosis Method for Gas Turbine Performance Parameters Based on t-SNE

This paper proposed a clustering method for gas turbine fault diagnosis. Our method is based on t-SNE and focuses on its clustering capability. The goal is to cluster the data of different health states of the same gas turbine to judge the time of failure. In order to strengthen the clustering ability of t-SNE for gas turbine data, we take the time factor into consideration based on the characteristics of the continuity of gas turbine health status. As t-SNE doesn’t actually provide class information but returns the results of dimension reduction, we use quick grid clustering to process the dimensionally reduced data and offer clustering results. We design a simulation dataset and demonstrate the effectiveness of our method through multiple experiments. Finally, we also apply this method to a real-world dataset and get a good result.
Decentralize Application for Storing Personal Health Record using Ethereum Blockchain and Interplanetary File System

Personal Health Record (PHR) is currently used as a communication platform between patients and health care providers that can be accessed at any time and must be secure. Blockchain based Ethereum that supports Smart Contracts and Decentralized Applications (DAPPS) supports secure transactions. Large medical record storage costs high on Ethereum so the Interplanetary File System (IPFS) as alternative storage is a solution to reduce costs. The built PHR function runs as follows. Users log into PHR to transact on blockchain smart contracts. When a medical record occurs, the PHR application will redirect the data storage to IPFS and return a unique hash value to the PHR application for permanent storage on the Ethereum Blockchain. The hash value used by this PHR application refers to medical records as the basis for consultations between doctors even in different hospitals based on patient authorization. In this way, interoperability between two or more different health information systems can be done safely and relatively inexpensively.
Application of sparse representation method based on K-SVD-ADMM in anomaly detection of satellite telemetry

In aerospace, anomaly detection based on telemetry data is a major task satellite health monitoring that is important for identifying anomalous events and for taking measurements to improve system reliability and safety. In this paper, a novel anomaly detection model using sparse representation method based on K-means Singular Value Decomposition (K-SVD) and Alternating Direction Method of Multipliers (ADMM) is introduced. The proposed K-SVD-ADMM is used to solve optimization problems and reconstruct the telemetry data. The anomaly score between the reconstructed and observed values is calculated and an adaptive threshold method is proposed to detect anomalies. Case analysis of satellite antenna component telemetry data shows that the proposed method is more stable and accurate compared with other existing anomaly detection methods based on One-class support vector machine (OCSVM) and Long Short-Term Memory (LSTM).
An Efficient Multi-Keyword Search Scheme over Encrypted Data in Multi-Cloud Environment

Recently multi-cloud has become the main model of cloud computing. With the rapid development of cloud computing technology, users are increasingly concerned about data security in the cloud. To ensure data security, users encrypt private data and upload it to cloud servers. Nevertheless, it is challenging to search ciphertexts with keywords from large amounts of encrypted data of multiple cloud servers. Moreover, existing attribute-based searchable encrypted schemes have several limitations, such as inflexible access control policy, only supporting single or conjunctive keyword search, and low search efficiency. Therefore, we propose an efficient Attribute-based Multi-keyword Search scheme (AMSE) over Encrypted data in multi-cloud environment. AMSE leverages the high-performance Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm to achieve multi-keyword ciphertext search and fine-grained access control. By introducing a retrieval server, AMSE can efficiently and accurately search ciphertexts in multi-cloud. The security analysis and performance evaluation demonstrate that AMSE is secure, highly efficient, and well-suited for multi-cloud.
Personalized Accurate Recommendation Algorithm of Ideological and Political Teaching Multimedia Resources Based on Mobile Learning

The currently used resource recommendation algorithm mainly recommends resources according to the user's preference for a tag class, ignoring the relationship between user preferences and needs and learning scenarios under mobile learning, resulting in poor efficiency and accuracy of recommended resources. In order to improve the shortcomings of the algorithm, this paper studies the personalized recommendation algorithm of Ideological and political teaching multimedia resources based on mobile learning. By constructing the map of Ideological and political teaching knowledge, this paper analyzes the correlation between resources. The diagnosis result of students' cognitive level is one of the characteristics of personalized recommendation. Mobile learning devices are used to collect data, calculate and perceive mobile learning scenarios. By improving the collaborative filtering technology, the teaching resources of Ideological and political courses can be personalized recommended. In the algorithm experiment, the average absolute error of the algorithm recommendation is relatively reduced by about 14.67%, the recommendation efficiency is higher, and the personalized recommendation effect is better.
Data Analysis of Non Fungible Tokens (NFTs) Pricing using Brokerage Firm data

In recent times, the rise of Non Fungible Tokens has been inevitable. An NFT is a special kind of cryptographic token that represents the ownership of a unique piece of digital property. They are tamper-proof due to the use of distributed public ledger that records verified information across a network of computers. With the rise of crypto-trading, the NFT market segment has seen an accumulation in its trading volume in the capital market. There are various factors determining the price and sales of the NFTs and there is a need for meaningful insights from the data generated intermittently over time. With this as motivation, this proposed research work explores the factors that create an impact on the NFT market and in-depth data analysis with the help of brokerage firm data. This proposed work helps NFT enthusiasts would be able to derive the correlation between cryptocurrency market and NFT market.
Construction Project Quality and Safety Management System Based on Blockchain Technology

Blockchain technology has unique advantages in quality management and traceability. It has had successful cases in food, medicine, e-commerce and other fields, but it is still less applied in the construction industry. According to the requirements of cscec-2020-z-12 funded by the science and technology R & D plan of CSCEC, this paper introduces blockchain technology into the construction industry and constructs a blockchain based construction project quality management system to help improve the quality of construction projects. Design the blockchain technical architecture, select the alliance chain to build the system, complete the setting of data layer, network layer, consensus layer, incentive layer and contract layer, and ensure the normal operation of the system. Then, four functional modules of the system are designed in the application layer, which are user management module, contract management module, quality management module and quality traceability module, and the business processes of quality management and quality traceability using the system are displayed. The experimental results show that the construction project quality and safety management system based on blockchain technology proposed in this paper has a shorter management time, a management range of more than 90%, and a strong management ability.
Which Keyword Extraction Method Performs Better for Emerging Technology Detection?

Detecting emerging technologies is a major concern for many researchers. In the process of emergence detection, we need to ha have some terms to assess them to be emergent or not. The demand of a method that can provide useful and meaningful terms but easy to implement made us to evaluate different automatic keyword extraction methods in the emergence detection process. In this study, we applied 11 automatic keyword extraction methods to detect emerging technologies in the field of Artificial Intelligence (AI) for the years between 2010 to 2019. These methods including Single Rank, Text Rank, Topical Page Rank, Position Rank, TFIDF, BERT, KPminer, RAKE, Topic Rank, RaKUn, and YAKE. All methods worked properly but RaKUn did not work well. Four methods of Single Rank, Text Rank, Topical Page Rank, and Position Rank performed better compared to other methods to capture more useful terms to be emergent. However, the BERT method was also successful in the process of emergence detection, especially for capturing highly emergent terms that other methods were not able to capture them. Therefore, using different methods such as Single Rank and BERT can be complementary. Additionally, the performance of the RAKE method to capture useful terms in a very fast way was also interesting, as it can be used in special situations where the data is big and the speed is important.
Recognition of Fricative Phoneme based Hindi Words in Speech-to-Text System using Wav2Vec2.0 Model

In this work, we have discussed issues with Microsoft's state-of-the-art Speech-to-Text (STT) system. Two key issues have been identified: recognition of Hindi words starting with the fricative phoneme (/ha/) and recognition power of the system with background noise. The solution for correctly identifying the unrecognized Hindi fricative phoneme is by training the Wav2Vec2.0 model on the OpenSLR Hindi dataset. The evaluation of the proposed model is given by the performance metric Char-acter Error Rate (CER). To test the performance of the proposed model, 20 fricative words in both clean and noisy conditions are fed to the trained model. The second issue of handling noisy speech samples is resolved using an amplitude-based automatic noise detection method. The results achieved from the proposed model are observed to be better than the state-of-the-art STT model when trained with and without the language model in terms of CER in clean conditions.
Energy-Based Learning for Polluted Outlier Detection in Backdoor

Big data analysis has become an essential tool in a lot of fields. An increasing number of entities rely on different kinds of data analysis tools to formulate their strategy. However, the popularity of big data brings several problems as well because attackers might pollute the data set by adding negligible data points to make a negative effect on the final analysis results. Therefore, in this paper, we propose to leverage the energy-based learning method to detect outliers within a data set. Specifically, we iteratively rule out bad data points from the data set based on specific selection rules. The experiment result is promising, which shows that our algorithm can improve the accuracy in the linear regression by more than 20% on average.
Grey Wolf Optimizer to the Hyperparameters Optimization of Convolutional Neural Network with Several Activation Functions

Hyperparameter tuning has gained a remarkable research interest since identifying a right configuration of hyperparameters gives an accurate model in deep learning. Convolutional neural network (CNN) is one of the most popular deep learning techniques that is widely used for image classification tasks. Obtaining the suitable hyperparameter configuration of CNN is challenging due to the mixed-variable types of hyperparameters and their high number of combinations in the search space. Manual search based on a trial-and-error approach is a time-consuming process and requires professional knowledge. Therefore, an automated search method is needed to reduce human efforts and to design a model with minimal human expertise. Motivated by these, in this study, the grey wolf optimizer algorithm is proposed to automize the process of finding hyperparameters of CNN, and several activation functions are adapted to the proposed framework to highlight the best CNN architecture. The experiments are conducted on the famous MNIST dataset, and comparative analyses are carried out among the models having different activation functions on the same problem.
Unsupervised Image Dehazing Based on Improved Generative Adversarial Networks

Image dehazing is a technique used for repairing blurry images which can effectively reduce the impact of haze on visual tasks. Most of the existing dehazing methods rely on atmospheric models or perform supervised learning based on paired images to obtain haze-free images. However, problems such as relying on prior knowledge of a specific scene and difficulty in collecting paired hazy and haze-free images have hindered the development of image dehazing techniques. In response to the above problems, we are inspired by the CycleGAN algorithm and propose the DAM-CCGAN algorithm, which uses an unsupervised method to dehaze unpaired images. For the blur and color distortion problems which can occur in image dehazing, the DAM-CCGAN algorithm adds a skip connection method and an attention mechanism module (DAM) to the generator. To preserve more image information, we add a detailed perception loss function. Meanwhile, to reduce the complexity of the algorithm, we improve the convolution group structure in the generator. Experiments show that our model achieves a good dehazing effect on both indoor and outdoor hazy images.
Power Load Curve Clustering based on ISODATA

Load clustering is the early basis of power grid system planning, load modeling, demand side management, load forecasting and other work. The traditional load classification method based on user types can not meet the needs of power grid services. Iterative Self-Organizing Data Analysis Algorithm (ISODATA) is an unsupervised learning dynamic clustering algorithm based on statistical pattern recognition. In view of the current problems that the initial clustering number of each algorithm is difficult to take and easy to fall into local optimum, the principle and implementation steps of ISODATA are introduced, and this algorithm is applied to the power load curve clustering. The clustering analysis is combined with specific power load curve samples, and the results prove that the clustering effect is better and the time improvement is larger. ISODATA is compared with the traditional clustering method to compare the clustering effect and the time loss of the algorithm. The results of the comparison experiments show that ISODATA has good clustering effect when applied to power load curve clustering.Isodata-based clustering of power load curves can fine distinguish users and provide decision support and scientific basis for the reliable operation of power system.
Bandwidth Prediction in 5G Mobile Networks Using Informer

Fifth-generation (5G) mobile networks aspire to deliver exceptionally high data rates with ultra-reliable and low-latency connectivity. With the growing popularity of mobile Internet and the increased bandwidth requirements of mobile applications, user Quality of Experience (QoE) is becoming increasingly critical. 5G networks demand predicting the real-time bandwidth of a channel to satisfy the QoE for bandwidth-savvy applications such as video streaming/conferencing, vir-tual/augmented/mixed reality, and autonomous driving. If future bandwidth can be forecast in advance, the bandwidthhungry applications may utilize the estimates to adapt their data transmission rates and dramatically enhance user QoE. By analyzing a publicly available 5G dataset comprised of the channel, context, and cell-related metrics with throughput information, existing work has used Long Short Term Memory (LSTM) based mechanisms to predict future bandwidth. We applied the Transformer-based model, namely ‘Informer,’ to the 5G dataset and found significant improvement of about 95% error decrease for bandwidth prediction. In addition, we combined some new feature analysis approaches (LASSO and Random Forest with new hyper-parameters) in addition to the the existing Random Forest with Informer to find out the most accurate prediction approach.
A survey of video human behaviour recognition Methodologies in the Perspective of Spatial-Temporal

Currently, video human behaviour recognition is the most foundational task of computer vision. The conventional recognition frameworks are building based on the images only, with current the wide usage of surveillance video as well as human behaviours are increasingly related to temporal information, video-based behaviour recognition has been widely studied by current researchers. This paper addresses multiple current research works and does the comparison between the variants of these algorithms. The methodologies are considered to be separated into traditional recognition and deep learning based methods which have reached the highest accuracy during current research works. Our paper does the comparison of existing frameworks and datasets which are related to video-type datasets only. We investigated multiple types of neural networks which are utilized for behaviour information extraction, as well as the challenges facing for our conventional and current methods. The deep learning based methodology for extracting both spatialtemporal information has involved plenty of frameworks. We compared the pros and cons of current existing works and provide the further research directions based on existing works
Priority Weighted Round Robin Algorithm for Load Balancing in the Cloud

Cloud computing, which helps in sharing resources through networks, has become one of the most widely used technologies in recent years. Vast numbers of organizations are moving to the cloud since it is more cost-effective and easy to maintain. An increase in the number of consumers using the cloud, however, results in increased traffic, which leads to the problem of balancing tasks on the loads. Numerous dynamic algorithms [1] have been proposed and implemented to handle these loads in different ways. The performance of these dynamic algorithms are scaled with different parameters, such as response time, throughput, utilization, efficiency, etc. The weighted round-robin algorithm is one of the most widely used load balancing algorithms. The proposed algorithm is an improvement of the weighted round-robin algorithm, which considers the priority of every task before assigning the tasks to different virtual machines (VMs). The proposed algorithm uses the priority of tasks to decide to which VMs the tasks should be assigned dynamically. The same process is used to migrate the tasks from overloaded VMs to under-loaded VMs. The simulations are conducted using CloudSim by varying cloud resources. Simulation results show that the proposed algorithm performs equivalent to the dynamic weighted round robin algorithm in all the QoS factors, but it shows significant improvement in handling high-priority tasks.
Improving the Performance of the Karbala Wastewater Treatment Plant by Using GPS-X Model

Long-term wastewater management has benefited from mathematical modeling. This is especially important when simulating complex biological processes. The purpose of this research is to model and simulate. the system at the Karbala wastewater treatment plant.is A2/O using GPS-X simulator to System Upgrade by pre- denitrification in different conditions. In this study, many external carbon sources were added to find out their effect on reducing nutrients from wastewater. Four external carbon sources were selected to improve the denitrification process and reduce phosphate concentrations for the current system. These compounds are acetic acid, propionic acid, methanol, and glycerol. These materials were added after the initial sedimentation basin and before the phosphorous removal basin, the best removal of nitrates and total nitrogen were observed when methanol was added. Where total nitrogen concentrations reached less than 10 mg/L when methanol was added. While the best removal of phosphate was when adding propionic acid. Glycerol was the weakest external carbon source by removing nutrients. But glycerin did not raise the concentrations of BOD and COD when added compared to the rest.
Optimizing the Pattern Recognitions Accuracy by Neural Network Methods

Due to the development of technology and computer science, especially in the field of artificial intelligence and machine learning, today information technologies have become the most important element in most fields. The developments in these technologies included building models and systems that depend mainly on machine learning. One of the most important areas that we need to develop is pattern recognition and classification. The term patterns include giving the computer the ability to recognize and classify images, shapes, sounds, and digital and textual data into different classes. These systems depend on building a model based on artificial intelligence and a trained neural network to analyze and classify patterns. In this article, we develop neural network algorithms based on five different datasets to build an intelligent pattern classification and recognition system by providing a neural network method for pattern recognition (NNM - PR). Using these algorithms, a high accuracy scale was obtained compared to previous studies.