Reviewerly/exHarmony huggingface dataset:
Towards a Blockchain Implementation of a Governance &amp; Revenue Dispersal Mechanism for Investments in Battery Energy Storage Systems

The rapid onboarding of renewable generation has led to many difficulties for both generators and the grid due to volatile output and market conditions. Battery energy storage systems have begun to alleviate these issues. However, such systems face many financial challenges in their deployment and operation. This paper demonstrates the use of advanced distributed ledger technology with smart contracts to design a decentralised autonomous organisation (DAO) to manage the investments in these systems and optimise their operations. In this paper, a series of smart contracts have been designed, deployed, and tested to demonstrate the effectiveness of such a DAO framework for battery management. The interconnected smart contracts can manage the financing, governing, and revenue-sharing mechanisms, allowing investors to coordinate investment and vote on the battery energy storage system's operation (charge/float/discharge) for a given period. This interconnected smart contract design implementation demonstrates a novel and effective strategy for managing battery energy storage systems, decreasing barriers to entry for investors, thus, allowing a greater adoption of such technology.
Net-Zero Energy Factories as Active Players in the Decarbonization Process. An Application for Blockchain

More than 32 % of small and medium enterprises in Europe generate electricity using solar resources. The operation of net-zero energy factories of such industrial prosumers can help system operators to integrate better into the grid. The application of blockchain technologies can help the industrial operator to generate extra revenue by providing flexibility services to system operators. This study analyses the role that facility systems can play if they are designed as net-zero energy factories and integrate blockchain-based technologies in their processes.
End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energy management systems. MM combines features from all-sky imagery and meteorological sensor data as two modalities to predict renewable power generation that otherwise could not be combined effectively. The combined, predicted values are then input to a differentiable optimal power flow (OPF) formulation simulating the energy management. For the first time, MM is combined with E2E training of the model that minimises the expected total system cost. The case study tests the proposed methodology on the real sky and meteorological data from the Netherlands. In our study, the proposed MM- E2E model reduced system cost by 30% compared to uni-modal baselines.
Research and Application of Business English Practice System Oriented to Cloud Computing Platform

In order to obtain better business English learning effect, an online business English practice system based on cloud computing platform is proposed. First, the business English online practice system framework of the cloud computing platform is designed, and the infrastructure layer is adopted to provide users with all facility services. Through the platform service layer, functions such as application development, testing and hosting are provided to users. According to the user’s knowledge points and question type scores, the software service layer evaluates the user’s business English learning status. The test results show that under the condition of concurrent application, the upper limit of the CPU occupancy rate of each function of the system in the research is about 13%, and the upper limit of the memory occupancy rate is lower than 8%. This value meets the criteria for computer application of the system in the research. Result finding, this system overcomes the limitations of the current English online practice system, has good fluency, low resource utilization, and can improve the learning effect of business English for users, with high practical application value.
A study on the application of graph neural network in code clone detection

With the increasing scale of software and the growing number of software developers, code cloning has become an important issue in software engineering. To detect code clones, this paper proposes a new method that converts source code into an Abstract Syntax Tree (AST), then adds edges from the Control Flow Graph (CFG) and Data Flow Graph (DFG) to the AST to form a graph. We then feed the graph into the Graph Matching Network (GMN) model and Pairwise Node Comparison (PNC) captures the edge and node information of the graph to calculate the graph similarity, thus converting code clone detection into a binary classification problem. Finally, experiments are conducted on the public dataset BigCloneBench, and the results show that the proposed method has high accuracy and scalability in code clone detection relative to other methods.
Solving the Performance Issues of Epsilon Estimation Method in Differentially Private ERM: Analysis, Solution and Evaluation

The study of differential privacy has recently garnered significant attention due to its ability to provide rigorous privacy protection for data analysis tasks. However, researchers are grappling with the critical balance between ensuring strong privacy protection and maintaining the utility of the model. While existing theories recommend selecting a privacy requirement and optimizing utility accordingly, practical product requirements often have strict accuracy constraints. As a result, privacy considerations may not always take precedence. Consequently, privacy guarantees are often adjusted to align with the expected level of utility. This mismatch highlights the challenge of training a highly private model that also meets the desired level of utility. Li et al. recently introduced a practical method for estimating utility at any privacy budget level, which can aid in achieving the aforementioned objective. However, upon analysis, our team has uncovered performance deficiencies in this approach. In this paper, we comprehensively examine the performance issues of Li’s method, identify their underlying cause, and present effective solutions. Our experimental results demonstrate that our proposed solutions can greatly improve the performance of Li’s method in a highly efficient manner.
Network Traffic Analysis Method Using the Fusion of Dynamic-Static Features based on Stacked Attention Mechanism

Network traffic analysis is a widely-adopted data fusion technology in network management and security, encompassing tasks such as malicious traffic detection and intrusion detection. With the proliferation of network users and emergence of new network services, network traffic analysis has garnered increasing attention. Current research methods primarily include machine learning analysis based on manual feature construction and neural networks for automatic feature extraction. However, manually designing features can be cumbersome and susceptible to errors, while shallow neural networks may not effectively learn feature relationships. The deep neural networks involve significant computational resources and may be susceptible to over-fitting. Moreover, both methods rely solely on a single feature source. To address these issues, this paper proposes a Dynamic-Static features fusion model based on stacked attention mechanism for network traffic analysis(DS-SAT). Next, we evaluated this method in five publicly available datasets and the experimental results show that our proposed method has achieved better results than other research methods.
Analysis of Multiple Information Collaboration Mode in Public Emergency Management Based on Blockchain Technology

This article explores the construction and optimization of a diversified information collaboration model for public emergency management based on blockchain technology. Firstly, the basic concepts and characteristics of blockchain technology were introduced, as well as its application in public emergency management. Then, a diversified information collaboration model for public emergency management based on blockchain technology was proposed, including information sharing, information transmission, information collaboration, and other aspects. Subsequently, the advantages and disadvantages of this model were analyzed, and its feasibility and effectiveness were verified through practical cases. Finally, the problems and challenges faced by this model were discussed, and corresponding suggestions and prospects were proposed. The research results of this article provide certain reference value for improving the efficiency and level of public emergency management.
A Task Scheduling Algorithm for Micro-cloud Platform Based on Task Real-time

To enhance the efficiency of different subsystems within embedded devices, thereby augmenting their capabilities, we incorporate cloud computing architecture within these devices to create an in-device micro-cloud platform. This allows subsystems, which were originally independent, to collaborate. Tasks on the device can be managed within a unified scheduling framework, thus increasing task flexibility and reducing task interaction overhead. Simultaneously, tasks on embedded devices often possess real-time requirements, an aspect often overlooked in previous cloud computing research. Accordingly, this paper prioritizes the real-time nature of tasks, studying a fair scheduling algorithm for tasks within the micro-cloud platform to address this hitherto neglected issue.
A Kannada Handwritten Character Recognition System Exploiting Machine Learning Approach

Handwritten character recognition plays an important role when the handwritten text on paper, postcards, etc. requires conversion of the handwritten text into digitized form. The difference between a digitized handwritten document and a scanned document is that the prior one can be edited, and the latter cannot. Significant developments have been made on the handwritten character recognition of widely used languages like English. India is a multilingual country where there exist multiple regional languages like Kannada, Tamil, Malayalam and other Dravidian Languages with complex scripts. Kannada is spoken in most of the regions of Karnataka State, which is one of the southern regions of India. In the proposed research, a Convolutional Neural Network(CNN) is practiced to recognize Kannada handwritten Characters. The research employs densely connected-convolutional networks or DenseNet variant of CNN to recognize handwritten Kannada characters. DenseNet is preferred in this research for its known advantages such as enhanced feature propagation, improved feature reuse, and minimized vanishing gradient problem. The dataset used in the experimentation is a standard Char74k dataset. The prime objective of this research is to devise a machine learning based application to recognize Kannada handwritten characters with high accuracy and convert them into digitized characters. Digitized documents promote the growth of several other major applications like speech conversion, language translation and conversion of medieval documents. A testing accuracy of 93.87% is observed for 3285 images of handwritten Kannada characters with 5 images from each of the 657 classes. This machine learning model can also be trained to recognize characters of different Indian languages.
Data Security Technology in Electronic Commerce System Development

Data security technology is an important aspect of e-commerce system development. With the increasing number of online transactions, ensuring that sensitive information such as credit card details and personal data is protected from unauthorized access has become a top priority. The use of encryption technology is a method of protecting data in transmission, ensuring that data is not intercepted by hackers. Another important aspect of data security in e-commerce systems is the implementation of access control. This involves restricting access to sensitive information to only authorized personnel who need it for their job function. In addition, regular audits should be conducted to ensure that all users comply with security policies and procedures. In summary, data security technology plays a crucial role in the development of e-commerce systems. It helps protect sensitive information from unauthorized access and ensures that customers can confidently transact online. With the continuous growth of online transaction volume, enterprises must prioritize data security measures to protect customer trust and loyalty. This article first analyzes the problems of data technology in e-commerce development systems, and then proposes construction strategies for business system development from the perspective of data technology.
Research on Distributed Multi-Task Learning System Based on Data Analysis Algorithm

This paper presents a research study on distributed multi-task learning systems based on data analysis algorithms. The paper starts by providing an introduction to distributed multi-task learning and data analysis algorithms, along with their importance in machine learning and artificial intelligence applications. The background section provides a more detailed discussion of these concepts, highlighting their specific applications and challenges. The paper then focuses on data analysis algorithms, discussing their types, features, and usage in different applications, including distributed multi-task learning. The paper also highlights some of the popular data analysis algorithms, such as clustering, classification, regression, and dimensionality reduction, along with their mathematical formulations and applications. The section on distributed multi-task learning provides an overview of its concepts, benefits, and challenges. The paper then discusses some of the popular algorithms used in distributed multi-task learning, such as Bayesian methods, transfer learning, and multi-task feature learning, along with their mathematical formulations and applications. The paper concludes by discussing some of the recent research trends in this field, including the use of deep learning and reinforcement learning in distributed multi-task learning systems. Overall, this paper provides a comprehensive overview of the distributed multi-task learning system based on data analysis algorithms, highlighting its importance and potential in various applications.
Intelligent Operation and Maintenance Schedule System Based on Knowledge Graph: Construction and Applications

Efficient operation and maintenance of data centers are crucial for supporting production management and information businesses in the power industry. However, addressing the intelligent information scheduling challenges in complex network systems requires specialized approaches tailored to the power industry's unique characteristics. This paper aims to address this issue by identifying the main problems present in existing intelligent information systems. Subsequently, a construction method for an intelligent operation and maintenance knowledge graph for cloud data centers in the electric power industry is proposed. Finally, an intelligent information scheduling system based on the knowledge graph is presented.
Symmetric and Asymmetric Cryptographic Approach based Security Protocol for Key Exchange

Recently, the security of confidential data over any communication channel has gained paramount importance. To tackle the problem of security breach, encryption and decryption techniques are widely used for attaining confidentiality of sensitive information which is transmitted over insecure channels. Symmetric cryptographic algorithms are more secure which use a key for both encryption as well as for decryption process but it faces a key exchange problem between the transmitter and receiver. This research paper presents a novel approach to address the key exchange issue which is encountered in symmetric encryption cryptographic techniques. A hybrid technique is proposed by using Advanced Encryption Standard (AES) from symmetric cryptography and Rivets Shamir Adelman (RSA) algorithms from asymmetric cryptography are used for designing a new protocol. This protocol achieves two major cryptographic primitives: confidentiality and authentication. The proposed hybrid encryption technique is simulated using Xilinx XST14.7 and encouraging design summary is obtained.
Fusion of Vision Based Features for Human Activity Recognition

The field of image recognition has made significant progress in recent years, particularly in the domain of Human Activity Recognition (HAR). HAR systems use computer vision and machine learning algorithms to classify images into different activities. A review of existing literature in this area was conducted, focusing on a vision-based system for recognition purposes. The present study focuses on recognizing Eating, Walking, and Chitchat activities using a vision-based approach. The dataset consisted of 1306 images, equally divided between the three activities, with around 400-500 per activity. This work has potential applications in surveillance, assisted living, elder care, healthcare monitoring systems, human-robot interactions, and gaming and entertainment. The model can also be embedded for monitoring elderly persons who are living alone and also to keep the track of their activities. The best possible results were obtained by using the Random Forest classifier after using the Fusion of Binary Robust Invariant Scalable Keypoints (BRISK) and Scale Invariant Feature Transform (SIFT) feature detector, with an overall accuracy of 89.41% and the precision of 89.42%, if the fusion of these features is not taken into consideration then solely Random Forest classifier provided the highest accuracy of 86.98% when applied on BRISK extracted features. The main finding of the study is fusion of BRISK and SIFT feature descriptors which showed promising results for all classifiers and they act as a novel approach in this proposed model and research.
Implementation of Secure Login and Access Methods for Web Frontend

This article mainly explores the implementation of secure login and access methods for web front-end. By using measures such as HTTPS encryption, multi-layer authentication, firewalls, access restrictions, and strong password policies, the security of web applications can be improved. We have achieved different results by implementing these strategies, among which using multi-layer authentication and HTTPS encryption is the most effective method. The data table and result analysis provided in this article can provide reference for developers to build more secure web applications. Finally, we emphasize that protecting the security of user information and application resources is crucial for the development and application of web frontends.
Analysis of Automatic Recognition Algorithm of Robot Visual Shape Features Based on Genetic Algorithm

Robot vision is an important aspect of computer science and artificial intelligence research. It mainly simulates human visual function through computer application technology, and analyzes and processes the information extracted from the image to achieve the actual detection and control effect. Firstly, this paper introduces the shape analysis method combining genetic algorithm and visual algorithm. Through the analysis of the results of the motion analysis experiment of the automatic mobile robot in linear motion, the actual theoretical speed is 0.199m/s, and the robot analyzes the turning motion of the mobile robot at a speed of 0.2m/s, and the actual turning speed is 0.2m/s, and the one-time optional turning is 360. Finally, this paper mainly analyzes and studies the feature recognition and classification methods of workpieces based on machine vision to help people better understand the classification and recognition technology of machine vision.
Research of Intelligent Tutoring System Based on Deep Learning Computer Technology

By analyzing the common problems in the teaching implementation of modern teaching system based on the background of network platform, combining the theory of deep learning theory and personalized learning theory, using artificial intelligence technology, the paper is aimed to design and analyze an intelligent learning guide system. Machine learning and deep reinforcement are made further research during learning and teaching. Its main object model, system structure and operation mechanism as well as the key problems of research are described. Through the experiment and comparation before and after, we find this kind of intelligent learning guide system can effectively solve the drawbacks of network teaching and realize personalized and intelligent teaching. It shows that the system promotes the development in this field and makes a great contribution to enhancing learners’ learning skills and improving the learning quality.
Design of Computer Software Engineering Machine Vision Platform Based on Traditional Image Algorithms

Machine vision systems can effectively improve production efficiency and accuracy. However, existing machine vision technologies are difficult to meet standards. Therefore, this article used traditional image algorithms to design a machine vision platform for computer software engineering. Traditional image algorithms can further improve the efficiency and accuracy of machine vision by denoising and enhancing images. This article designed a computer software engineering machine vision platform based on traditional image algorithms, achieving an average efficiency of 85.6% and an average accuracy of 94.6% for machine vision. The final conclusion drawn in this article is that the application of traditional image algorithms can effectively improve the production efficiency and detection accuracy of machine vision platforms.
SRNN-MAFM-based Unimodal Text Sentiment Analysis

Sentiment analysis, a crucial task in natural language processing (NLP), involves extracting subjective information from text data to understand public opinion, market trends, and brand reputation. This research introduces a groundbreaking unimodal text analysis system called Stacked-Recurrent-Neural-Network with Multi-level Attention and Feedback module (SRNN-MAFM) for sentiment analysis. The proposed system employs advanced techniques to perceive sentiment in texts and categorizes them into three extreme sentiments: positive, neutral, and negative. The sentiment analysis module utilizes a Boolean structure and an OR operation for classification. The study includes a Python platform simulation using the STS-Gold datasets to evaluate the proposed technique. The simulation results demonstrate that the proposed unimodal text sentiment analysis technique achieves an impressive accuracy of 97.8%, outperforming existing methods. This research contributes to the advancement of sentiment analysis and provides a highly accurate and efficient solution for analyzing sentiment in unimodal text data.
Transformer Based Speech to Text Translation for Indic Languages

In this study, we are looking into additional ways that could help us enhance our output from Speech recog-nition models. We’re specifically interested in improving the language model (LM) to improve the current accuracy. Rare words continue to be a challenge in developing high-quality speech recognition systems because words based on names, proper nouns, or localities, often called tail words are crucial to the decoded transcript’s meaning. They are difficult to handle correctly since they do not appear frequently in the audio-text pairs that make up the training set. Using the transformer architecture, utilizing better datasets and finetuning can help us achieve a more sustainable model.