Reviewerly/exHarmony huggingface dataset:
Review of an Agile Software Development Methodology with SCRUM &amp; Extreme Programming

Software development methodologies offer a methodical way to working on projects in accordance with client expectations. In the software development process, the customer is seen as the main entity. In order to build valuable software quickly and within a given budget, firms are now seeking to implement customer-centric techniques. Customer satisfaction plays a crucial part in the development and deployment of software products. One software development process that helps to best fulfil changing customer requirements is agile software development. In the following part, a table of comparisons between the various agile modelling methodologies with regard to requirements and the role of the customer follows a discussion of agile methodology. The Scrum & Extreme Programming framework is presented, along with how different firms use the approach for projects of various sizes.
A Cloud based Mobile Application to Hire Unskilled Workers

In this modern era, the Majority of individuals are preferring mobile applications for the completion of their work. Generally, there are some applications that can hire skilled workers. People living in cities cannot directly contact the workers. The proposed system will help people to hire workers on a daily/contract basis. It gives everyday work to the workers who will be idle. This system will help both Employers and workers as well. This proposed system offers services specifically for laborers in the construction industry. Workers and Employers have to register and login into the system. The worker profile page contains the details such as location, types of work, activity status, and demand wages. Whereas, Employer profile page contains the details such as work location and personal information. Payment can be done offline. Work providers can rate the worker's performance.
A Neural Network Pruning Approach to Human Activity Identification Based on Deep Learning

In big data applications such as ambient healthcare-supported living, accurate human activity identification may be of tremendous use. In current years, research that is associated with the study of human behavior has been the focus of greater interest among engineers working in the field of computer vision, in particular research that makes use of deep learning. Deep learning (DL) strategies have made significant contributions to the advancement of research in HAR (Human Activity Identification). When it comes to the automated feature extraction, these deep learning algorithms perform far better than traditional machine learning (ML) approaches. In recent years, a great number of DL models have been shown to be state-of-the-art systems that can effectively identify basic and complicated human actions in order to deal with the HAR. In trained neural networks, pruning techniques of neural networks may reduce the number of available parameters, which in turn improves the computing speed of inference, all without compromising correctness of the model. By applying the neural network pruning approach to human activity identification based on deep learning, this study demonstrates its usefulness. PAMAP2 data were analyzed using an architecture called long short-term memory (LSTM), which was used to identify everyday human activities. Before training a LSTM network, the data was preprocessed. PAMAP2 is a publicly available benchmark complex activity dataset that is used to assess the proposed architecture. The results of the experiments indicate that suggested LSTM model achieves the best level of accuracy (97.42%) on the same dataset as other baseline DL models, showing a considerable improvement over other model. The excellent performance of LSTM was also validated by our investigations when comparison to the performances provided by the other base models utilised. The findings of the experiments demonstrate that the approach we have suggested is capable of accurately predicting the social actions of humans.
Analysis of Personality Traits of Handwriting with Comparison of Different Techniques of Personality Detection

Combination of characteristics and qualities of an individual is called personality. Personality of a person can be judged by various parameters; it varies according to the environment of the person; inter relationships with the surroundings, past experiences of life, nature, and skills. Handwriting is a unique feature of each person, which helps to detect the person's behavior, and certain psychological aspects. Graphoanalysis is a process to analyze personality based on handwriting. In this paper we analyze different research studies of personality identification based on handwriting. There are many features of handwriting like baseline, strokes which demonstrate psychological characteristics of the writer. In this research we analyze different states of art techniques of personality detection. This study gives a detailed review of many parameters of Handwriting which are examined for personality prediction.
Solar Power Forecasting Based on Numerical Weather Prediction and Physical Model Chain for Day-ahead Power System Dispatching

Accurate solar power forecasting has a decisive effect on the formulation of day-ahead power system dispatch strategies. At present, there is every confidence that paring numerical weather prediction with a physical model chain is the state-of-the-art solar forecasting method suitable for grid integration. Leveraging this two-stage solar power forecasting framework, the total cost benefits of day-ahead power system dispatch have been quantified based on a modified IEEE 24-bus system. The numerical experiment results reveal that the two-stage forecasting method saves 2.1 million dollars (1.33%) per year, as compared to the benchmark.
Covid-19 Vaccine Management System based on Blockchain: A Survey

Early in 2020, the coronavirus Covid-19, which is produced by the SARS-CoV -2 strain, first gained international attention as a severe health threat. Covid-19 spread quickly around the world, forcing everyone to fight with preventative measures like masks, hand washing, and preserving social distance. But to prevent the virus, vaccination has been playing a key role. Vaccination records that contain patient data make this system very complicated because there is a risk of a privacy breach. Hackers may steal the personal health information of individuals or may carry out cyberattacks against any national health data server. Additionally, there is a chance that dishonest people can purchase and sell fake vaccine certificates on the black market. Blockchain can provide a solution to this regard by its features like data immutability, privacy, transparency and decentralization. For people, governments, and organizations interested in blockchain-based systems, we analyze the blockchain based vaccination management system in this study and provide a current summary. We envision our study to motivate more blockchain based systems.
LabelScr: Automated Image Annotation on Mobile and Web Applications for Object Detection and Localization Model Pipelines

Object localization and detection, as an emerging and demanding subject in the computer vision community, is critical for creating next-generation computer vision systems, and over the past decade, it has received a lot of attention. Deep learning requires large amounts of data, and building datasets for this task is not a simple operation. Recent developments in the field of computer vision are mostly driven by the use of deep learning technologies. Detailed processes with several phases and users with varied roles are common in larger benchmark datasets during annotation. However, in smaller projects with limited resources, this can be difficult to implement. Therefore, this work presents a process for creating an image dataset for mobile apps (Android/iOS) and web apps without the need for manual annotation of images. These datasets can be used to build models that can detect and localize icons and other objects available on digital screens. Segmentation of different parts on digital screens can also be achieved using a dataset created using this method.
Graph Computing Based Electric Power Equipment Defect Grading with Multi-scale Mechanism

Power equipment inspection aims to discover and eliminate equipment defects and potential safety hazards timely and ensure the safe operation of equipment. Now a large number of graph structure data have emerged in the equipment inspection field to improve the organization, management, and cognitive ability of knowledge. As a key step in the inspection business, equipment defect grading is used to determine the severity that affects the safe operation of equipment, so the accuracy and generalization ability of the model are required to be high. The pooling operation of traditional graph neural networks has the problem of information loss. Therefore, this paper introduces the multi-scale mechanism to improve the accuracy and generalization ability of the model and realize the accurate grading of equipment defects. By conducting experiments on NCI-1 and CEPRI_EQUIP datasets and comparing the proposed algorithm with baseline methods, the significant performance of the proposed algorithm is verified.
Progressive Feature Fusion and Refinement Network for Substation Rotating Object Detection

Real-time substation object detection is of great significance to ensuring the safe and stable operations of the power grid. Considering that the substations are complex in the background and the targets are distinct in sizes, shapes and rotation angles, we propose a progressive feature fusion and refinement network (PF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RNet) for substation rotating object detection. In the network, ResNeSt50 is used as the backbone to improve the feature extraction ability, and the deconvolution feature fusion module is designed to generate richer semantic information. To perform better in substation scenes, the rotating anchors are used to reduce the Intersection over Union between anchor boxes. Besides, the feature refinement module is introduced to realize the regression process from coarse to fine, strengthen the feature information of the object location, and then alleviate the feature misalignment. Finally, experiments demonstrate that the mAP of PF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RNet on the substation multi-object dataset reaches 89.3%, which is improved by 5.2% compared to RetinaNet.
Short-Term Photovoltaic Power Combination Forecasting Method with Time-Varying Weights

This letter presents a variable weight short-term photovoltaic (PV) power combination forecasting method based on similar numerical weather prediction (NWP)data. This method firstly identifies and reconstructs the abnormal data, and divides the photovoltaic power data after data processing into the training set, intermediate set, and test set together with NWP data. Secondly, the parameters of single prediction models are optimized by training set data. Thirdly, in the intermediate set, based on the prediction results of single models and the actual photovoltaic power data, the optimal weight coefficient at each time is obtained by constructing the objective function and constraint conditions. Lastly, by matching the NWP data of the intermediate set closest to the NWP data to be predicted, the corresponding weight coefficient set is extracted to determine the time to be predicted. Several experimental results are provided, demonstrating the superiority of the proposed variable weight combination model over both fixed weight combination models and single prediction models.
Ultra-short-term Probability Prediction Method of Photovoltaic Power Considering Satellite Image

As cloud shielding is one of the main factors leading to the fluctuation of photovoltaic power, an ultra-short-term probability prediction method of photovoltaic power considering satellite cloud images is proposed. Firstly, a cloud displacement vector algorithm for the outer edge of the cloud cluster is designed, based on the block-area-center-positioned strategy, which improves the calculation efficiency and precision; Then, based on the Gaussian mixture model of multi-station output, a conditional probability model considering cloud occlusion is constructed. The model can give the probability prediction of power for target photovoltaic station, using the historical power of reference stations and predicted power of target station. The model corrects the correlation of adjacent power stations by introducing time delay, which is calculated using the result of cloud displacement vector and Pearson correlation coefficient of power. The cases show that the proposed method is superior to the existing probability prediction methods both in reliability and sharpness.
Research on Blockchain Electric Energy Transaction Based on NSGA2 Genetic Algorithm

In the traditional electricity trading model, centralized transaction data and centralized regulatory agencies will bring about problems such as low data security and a crisis of trust in regulatory agencies. As an emerging distributed shared database technology in the Internet era, blockchain is related to the characteristics and it have a natural fit with the energy Internet, which brings a development opportunity to the traditional power transaction. This paper selects the microgrid operator (MO), energy aggregator (EA) and large power user (LC) as the distribution side the main body of electricity trading, built a power distribution side power market trading platform under blockchain technology, with the goal of maximizing benefits and minimizing costs, and established an active distribution network power trading optimization model based on cooperative evolutionary games,and use NSGA2 genetic algorithm to solve. And on the basis of the obtained optimization results, the Shapley value method is used to distribute the benefits of the power purchase cost of large consumer. The solution result of the optimization model is the basis for the generation of the smart contract. The calculation example results verify the feasibility of the smart contract model and the functional characteristics of the blockchain as a distributed ledger.
Research on Information Assets Security Management of Electric Power Enterprise Based on Cloud Edge Collaboration Technology

With the advent of the information age, the high-speed development of the power industry is increasingly dependent on the information network, and the problem of information security is prominent. In view of the threats of information leakage and damage caused by external network attacks and internal mismanagement of power grid companies, based on the study of the characteristics of Electric Power Enterprise information asset security management and the analysis of the weak links of Electric Power Enterprise information security management, a Electric Power Enterprise information asset management system based on cloud edge collaboration technology is proposed, and the design of key functional modules of the information asset management system is given, It can provide feasible scheme guidance for the security management of Electric Power Enterprise information assets and promote the improvement of information security management level of Electric Power Enterprise.
Mixture Loss Function-based Classification Network for Few-shot Learning

Data augmentation technology can effectively solve the problem of few-shot image classification, but many approaches based on data augmentation generated sample have poor discriminability, which negatively affects the classifier and classification accuracy needs to be improved. In this regard, based on the TFFCN, a mixture loss function-based classification network for few-shot learning (MLFCN) is proposed. First of all, for the prototype tensor features with a simple mean representation, ignoring the different contributions of the sample features of support set for per class to the class prototype tensor features, so that the generated prototype tensor features can not well represent sample features of per class, affecting the discriminability of the generated samples. Introduce the improved adaptively spatial feature fusion module, it uses the convolution, considers the support set of different sample features on the prototype tensor, adaptively assign weights, then weighted and summed to obtain a more representative prototype tensor feature; secondly, aiming at the blurred classification boundary of similar categories in the generated sample features, a mixture loss function is proposed. The loss function is to add the inter-class mean squared error term to the mean square error loss, and stretches the inter-class distance of the generated tensor feature, so that the classification boundary is clearer, so as to train a better classifier. Experimental results show that the improved adaptively spatial feature fusion and the mixing loss function can augment the discriminability of the generated tensor features and further improve the classification accuracy.
Construction of a Decentralized Management System Based on Blockchain Technology

The construction of innovation in all fields, conduct the thorough research to the widely used block chain technology, and combined with artificial intelligence, cloud computing, big data and other advanced technology innovation, not only showed more technical advantages, such as anonymity, security, decentralized, etc., also solve the problem of data provides a new train of thought for people, gradually get rid of the limitation of traditional centralized. Therefore, this paper takes the current performance management of colleges and universities as an example, mainly using decentralized blockchain technology to build a new management system. On the one hand, P2P network and block chain technology microsystem operation to build a safe and stable environment, on the other hand, the results of college students into the block chain, provide users and data interactive interface, thus with query, modify, upload and other basic functions. The final test results show that the decentralized management system with blockchain technology as the core can not only ensure the safety of the system operation, but also orderly transfer and search related information.
A Recursive Descent Analysis-based Solution for Smart Contract Generation and Management

In the field of supply chain, due to the involvement of multiple business entities, incomplete credit mechanism and information asymmetry have been the difficulties in supply chain that need to be solved. With the development and advancement of blockchain technology, the core technology in blockchain - smart contracts - has attracted much attention. The technology automates the execution of contracts on the blockchain in the form of code, allowing trusted transactions to take place without a third party, which are traceable and irreversible. The technology involves collaboration between different fields such as computer science and laws, but most traditional professionals lack programming skills, resulting in the current application of smart contracts can not meet cross-industry demand. This paper propose a method to generate smart contracts automatically. Users can model the contract by OWL(Web Ontology Language) and write SWRL(Semantic Web Rule Language) semantic rules to describe contract. Then use recursive descent analysis to implement a LL(1) syntax parser that recognises SWRL and generates the corresponding smart contract code based on the semantic rules of SWRL. The contract management module allows users to generate, install and manage smart contracts directly from the front-end page, which lowers the threshold for users to manage the blockchain network. We validate the usability of the contract generation and management module using the compliance inspection of RMC (ready-mixed concrete), which is based on HyperLedger Fabric. The system can generate smart contract code that corresponds logically to the quality specification.
Cloud Storage Ciphertext Search Scheme Based on Blockchain

The fast search of cloud storage encrypted documents is a research hotspot of searchable encryption at present. However, with the increase of the amount of data, the retrieval efficiency of the cloud server continues to decrease, and its security is difficult to be guaranteed. In order to improve the security and reliability of data in cloud storage, this paper studies the verification of multi-keyword search and document tamper-proof. A novel multi-keyword verifiable search encryption scheme is proposed, which combines blockchain smart contract, local sensitive hash algorithm, asymmetric encryption algorithm and other technologies to achieve fast search and security verification of cloud storage encrypted data. The experimental results show that the scheme can not only ensure security and query accuracy, but also improve query efficiency.
Turbulent Effects on Continuous-Variable Quantum Key Distribution

Gaussian modulation of quantum coherent states are more adaptable with traditional optical communication equipment and have better compatibility with fiber channel in the spatial continuous variable quantum key distribution (CVQKD). We construct a spatial CVQKD model to analyze the effects of turbulent channel parameters, such as beam spreading, absorption, scattering and drift. The model innovatively introduces the role of the phase difference jitter between local oscillator (LO) and quantum signal light. The findings demonstrate a positive correlation between the excess noise and the transmission distance, turbulence intensity, and the time between the LO and the quantum signal light. The secret key rate decreases with the increase of delay time. The simulation results can provide a reference for the design and optimization of the actual spatial CVQKD system.
Insulator Defect Detection and Positioning Method in Power Grid Inspection

With the innovation and development of social economy and science and technology, transmission lines have gradually emerged the characteristics of long distance development, high power, high voltage and so on. However, due to the complex environment, it is very easy to appear aging, natural damage and other problems, which directly affect the safety and stability of power grid system operation. Insulators are one of the most widely used parts in the power grid system. How to accurately locate, detect and analyze the defects of insulators in the power grid inspection is the main problem explored by the power industry at present. Know the inspection in this paper, we study the status quo, on the basis of insulator defect detection technology at home and abroad, according to research results, the deep exploration to the BP neural network as the core of defect classification testing and inspection images for unmanned aerial vehicle (uav) is the core of image processing technique, finally the experimental results show that the two methods can accurately identify the insulator at the same time, Help staff quickly find the location and solution.