Reviewerly/exHarmony huggingface dataset:
A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction

Accurate photovoltaic power forecasting provides essential information for the flexible control of building energy systems. This paper proposes a physical model with environmental parameter prediction and an improved maximum power point tracking algorithm for hourly photovoltaic power forecasting. This study incorporates the forecast of meteorological parameters into the photovoltaic model so that the physical model proposed in this paper can achieve photovoltaic power prediction in the face of different weather conditions compared to previous photovoltaic physical models in the literature. A hierarchical clustering algorithm was used to obtain future hourly irradiance based on different weather conditions. The coordinate analysis method was used to calculate the hourly irradiance received on the surface of the photovoltaic panel. An equivalent circuit model was used to calculate the current-voltage characteristics of photovoltaic panels. Finally, the output voltage of the photovoltaic panel was adjusted by the improved maximum power point tracking algorithm to obtain the photovoltaic power. This algorithm can accelerate the calculation process and avoid long convergence times or oscillations near the optimal value. A photovoltaic project located in central China was selected as a case study to verify the accuracy of the prediction model. The long-term monitoring results show that the relative error of the predicted irradiance of the photovoltaic panel surface is 18.5%. The mean absolute error of the forecasted photovoltaic power was 15.9% in 120 consecutive days under various weather conditions, indicating that the model had high accuracy compared with traditional machine learning methods.
VHNet: A Video Hiding Network with robustness to video coding

Data hiding is an essential practice for ensuring the security of confidential data and secret communication and thus has been intensively studied. The development of deep learning enables the realization of video hiding, which aims to hide a video into another. Different from image data hiding, stego videos generated by video hiding methods need to undergo video coding in real-world scenarios because the unencoded raw videos are too large to be easily transmitted or stored. However, previous video-hiding works did not consider this, leading to a common vulnerability to information loss caused by video coding on stego videos. In other words, previous works considered videos simply as sequences of temporally continuous images, and thus the image sequences output from the data hiding network were fed directly into the recovery network to obtain secret information without video coding. We propose a module called CompressionSimulator to make stego videos robust to video coding. CompressionSimulator is pre-trained to simulate the distortion caused by video coding, and then trained together with the whole framework to make the data hiding network robust to video coding. Furthermore, an invertible neural network based framework is introduced for our video hiding task, i.e. hiding one video in another, in which we design an auxiliary module to capture the spatio-temporal relationships of videos. In general, we develop a video hiding network (VHNet) that provides video coding robustness. We conduct experiments to prove the necessity of incorporating video coding into the design of video hiding networks. Results of experiments conducted on the UCF 101 dataset show that our proposed work outperforms previous methods, and our framework achieves optimal performance in both visual and quantitative comparisons. To the best of our knowledge, we are the first paper to propose a learning-based scheme to improve the robustness of hiding videos with videos.
BCRNet: Bidirectional contrastive representation network for deep multimodal learning of exercise representations in online education systems

In online education systems, learning exercise representation is a fundamental task in many applications, such as exercise retrieval and recommendation. To capture the heterogeneous data information of exercises (i.e., texts and images), deep multimodal approaches show the promising performance. However, these methods have two limitations: (1) they only care about context on one side, which fails to utilize the future context chunks in the exercises; (2) they cannot ensure the presentation ability due to the scarcity of labelled data. In this paper, we propose a bidirectional contrastive representation network (BCRNet) to tackle these issues. First, we construct a representation module with a masking constraint loss to take into account the bidirectional context contents of exercises. Second, we design a contrastive learning approach which uses a multimodal contrastive loss to reshape the multimodal representation space and improve model presentation ability without labelled data. Moreover, a text-image matching strategy is designed to provide semantic links between texts and images. On the real-world dataset, experiments demonstrate BCRNet performs significantly better than many strong baselines.
FedInf: Social influence prediction with federated learning

Social networks are ubiquitous in contemporary society, where one’s opinions and behaviors are easily influenced by those around them. The effectiveness of social influence analysis has become significant for numerous application fields such as online recommendation, advertising, and viral marketing. Graph-based deep learning has achieved significant success in influence prediction. However, the existing methods ignore the privacy issue and are incapable of cross-organizational collaboration. Inspired by the recent success of federated learning in privacy protection and breaking the data barrier. In this paper, a novel federated learning framework, FedInf, is proposed to tackle the problem of social influence prediction. Specially, to further protect user privacy and achieve secure aggregation for multiple local models, we introduce differential privacy into the local parameters, adding artificial noise before model aggregation. In order to trade-off utility and privacy, we first freeze the embedding layers to reduce the number of upload parameters, and then project model parameters into low-dimensional space. Therefore, less noise is required to provide the same level of privacy protection. Extensive experiments on four real-world datasets demonstrate that the proposed FedInf is effective while providing privacy protection.
Research on unbalanced class learning method based on logistic regression mixed strategy

In the context of the era of big data, machine learning and pattern research are the main contents of the technical discussion of scholars around the world. Faced with the continuous increase of data information, the problem of class imbalance appears in the relevant technical research. The main feature is that the number of instances of some classes is obviously less than that of other classes. From the Angle of practical application, in cases of hospital diagnosis, for example, because only a handful of cancer patients, so how to correctly identify all kinds of mass data information in cancer patients, practice can improve work efficiency, and can quickly find conform to the requirements of the case, to modern medical diagnosis technology research is of great significance. Therefore, on the basis of understanding the status quo of modern technology research and development, this paper, according to the relevant theories of unbalanced data set and logistic regression model, deeply discusses the unbalanced class learning method with logistic regression mixed strategy as the core. The final experimental results show that the new logistic regression algorithm can effectively improve its performance in class imbalance on the basis of guaranteeing high accuracy. Compared with other advanced methods, the logistic regression model has obvious technical advantages.
IoV Data Processing Algorithms for Automatic Real-Time Object Detection - Literature Review

With the advent of the Internet of Vehicles (loV)., there is an increasing demand for real-time and accurate object detection algorithms for intelligent transportation systems. This literature review aims to provide a comprehensive analysis of the latest research in loV data processing algorithms for automatic real-time object detection. By analyzing and comparing various state-of-the-art algorithms such as Faster R-CNN., Mask R-CNN., and YOLOv4., the strengths and limitations of each approach are identified. Additionally., the challenges associated with real-world implementation are discussed and provide recommendations for future research directions. This research study highlights the importance of efficient and accurate object detection algorithms in the context of the loV and emphasizes the need for ongoing research in this field.
Voice based Autonomous Pill Assistant for Alzheimer Patients

The main objective of this study is to assist Alzheimer's patients in providing the pill at the right time. It is necessary for the Alzheimer patient to have medical support at all times since the Alzheimer patient might quickly forget things they do every day such as taking medications, eating, and so on. In the case of tablets, if the Alzheimer patient forgets to take the medication, he/she may suffer from serious health problems; on the other hand, if the Alzheimer patient forgets that he/she has already taken the medication and takes it twice or three times, the patient may suffer from serious health problems. To address this issue, this research study has designed a novel system to administer pills only at the specified time and only when they are necessary to be consumed. Furthermore, while the tablet is being dispensed, a voice alert will be given to the Alzheimer patient to remind them to take the required pill. Also, if the patient becomes critically ill and requires immediate assistance can press the emergency button.
Prevention of Spambot Injection in Web form on Cloud Platform

The primary goal of this Research is to identify preventative measures to halt spam that may occur in web applications hosted on cloud platforms. Many intrusion-based tactics, such as bot spamming in a cloud computing environment, can be used to spam web forms. Attackers frequently deploy spam-bots in advance modules as one of their methods for spamming cloud-hosted websites. A method of intrusion prevention is required to prevent these kinds of problematic situations from occurring in various ways. In the modern era, adding a honeypot field to the form is commonplace along with the captcha technique, which, while reducing the amount of spam, isn't entirely eliminating it. The goal of this project is to find a solution to problems that honeypot and captcha cannot solve. This can be overcome by generating a dynamic token and place it in the front end on the webpage. This prevents the spam bot from injecting the bad leads in the web form.
Study on Distributed Multi-Level Security Scheme For Cloud Computing

Cloud computing seems to be a scalable and realistic distributed computing platform that delivers resources as a service. One of the major difficulties impeding cloud computing development is data security in the cloud. The security vulnerabilities in cloud computing and other cloud services slow down cloud computing growth and difficulties with data protection and privacy. In this new context, cloud service customers must be cautious about data breaches. This article studies a solution to current diverse security challenges that have arisen as a result of the service demonstrating that the offered methods are provably safe and very efficient. The solution comprises a distributed multi-level security scheme for processing the information involved in cloud computing. The proposed scheme evaluates the most significant encryption techniques generally used in various industries like AES (Advanced Encryption Standard), Blowfish, 3DES (Data Encryption Standard), CAST128, and RC4 (Rivest Cipher 4). The proposed model with these encryption techniques ensure minimal energy consumption in cloud computing with maximum speed. Moreover, to increase the security of the data, various cryptographical algorithms are applied, analyzed, and compared in an OpenStack environment.
Visual Analysis of Project Investment Decision Information Platform based on FCM Algorithm

This study conducts a visual analysis of project investment decision information platform based on the FCM algorithm. The FCM algorithm has the advantages of simple principle and high accuracy. At present, the research mainly focuses on the simple scenarios where the FCM algorithm needs to artificially specify the number of clusters. Hence, for the better improvement of the model, novel data analysis model is considered to enhance the traditional FCM. The visual analysis of decision making is selected as the testing scenario. Based on the experiment, the overall performance is validated.
Towards Challenges Faced in Agile Risk Management Practices

Agility is a necessity for enterprises as a result of their critical need to respond swiftly to a fast changing environment. “Tomorrow problems are today's risk.” So, a problem that may result in some damage or endanger the progress of the project but has not done so is a “risk,” according to the definition. These potential problems that occur could also have a bad impact on the technical success, project's budget, timetable the product's quality, or the team's morale. The process of recognizing, addressing, and removing these issues before they affect the project is known as risk management. We must distinguish between risks, which are future difficulties, and the project's ongoing challenges. A lot of challenges might be there in project. It's crucial to sort different risks into different categories in order to systematically identify the key risks that might have an effect over the software project. Optimizing the traditional risk modelling approach to investigate software system vulnerabilities is the main goal. Using machine learning techniques improves the use of data generated during software development processes. To improve current risk modelling techniques, a lot of industry knowledge about security vulnerabilities can be used. In this paper the study of challenges with risk management is initially presented and the attitudes towards these issues are then elaborated, building on the features of how these challenges can be solved. Ultimately, solutions to each of the challenges are given based on the traits The work done here is an integral component of Agile-based software development processes. Based on the notions derived from how the studied literature has tackled each of the difficulties, five steps were determined and they are as follows-Collection of the possible risk factors Refinement of risks; Risk mitigation; transfer of knowledge and escalation. The conclusion described here could be used as an initial point to guide proj ect developers and the managers in structuring their proj ects with cybersecurity risk management in mind, promoting less overburdened agile processes, stakeholder insights on pertinent issues.
Intelligent Analysis Framework of Power Marketing Big Data based on Intelligent Word Vector Algorithm

Intelligent analysis framework of power marketing big data based on intelligent word vector algorithm is the focus of this study. In information retrieval, the traditional framework regards keywords as a unit to reveal the topic of the text, and the number of indexing keywords is suitable to be within multiple words. Based on this knowledge and the results of manual analysis of some samples. The designed framework contains the 2 core aspects. (1) For the data pre-processing step, data cleaning algorithm is considered. (2) For the data processing steps, the data retrieval is the essential algorithm. The combination model is constructed for taking the efficient analysis of the power marketing big data analysis. The word vector algorithm is simulated and tested.
Detection and Comparative Analysis of Handwritten Words of Amharic Language to English using CNN-Based Frameworks

Amharic is made the official working language of Ethiopia from the late 12th century onwards. Amharic is a language with a total of 57.5 million speakers, out of which 32.4 million people are being the first-language speakers or native speakers and remaining are alternative-language speakers. Amharic is a distinctive syllabic writing system that employs 33 consonant symbols and each one's seven vowel variants. Due to the ambiguity among constant symbols, it is difficult to convert handwritten simple words in Amharic to English. This research study focuses on detecting the text in Amharic words in handwritten images and converting them to English text using various Convolution Neural Network Based frameworks such as AlexNet, Lenet-5, and ResNet-152 available today. For that, a new dataset has been created with more than 60,000 training and 10000 testing images of Amharic constants written by various age groups and named it LBRAMHARIC. After that, various CNN-Based frameworks are trained with LBRAMHARIC and tested on the random inputs and customized input images. In the end, a comparison has been made on the obtained results by considering the different evaluation criteria such as accuracy and prediction rate.
Word Embedding for Bengali Language using Domain-related Corpus

Many natural language processing (NLP) tasks, including machine translation, document classification, information retrieval, news category classification, document clustering, news category clustering, and question-answering systems require the use of distributional word vector representation which is a very low dimensional vector representation of words that is called word embedding, such as contextual and non-contextual embedding. In addition to several contextual word-embedding approaches like BERT, IndicBERT, and SahojBERT (the Bengali equivalent of BERT), this work also covers numerous embedding strategies (such as Word2Vec, GloVe, and FastText) with multiple hyper-parameters. Of late, word embeddings from general corpus such as Wikipedia dump, and common crawl corpus are very famous to make word vectors. In the case of Indian languages also such type of corpus is used to make word vectors. But it suffers in giving good accuracy in general NLP tasks like Sentiment Analysis, News Category Classification, and News Category Clustering, especially for the Bengali Language. This research demonstrates, most importantly, that word embedding from a domain-related corpus enhances the quality of the embedding as those embeddings are working far better than general pre-trained word embeddings which can be used in various NLP tasks like News Category Classification, Word Similarity, and Document Clustering like News Category Clustering using various clustering techniques like KMeans, KMedoid algorithms and also obtaining better accuracy. This work proves that making word vectors or word embedding from a domain-related corpus for a particular NLP task derives better results alongside the quality of those embeddings. Throughout this work, a Deep Neural Network model was used. And the same model was used in all the tests and findings in this work. This work clearly states if word embedding can be derived from a corpus that is related to the domain on which NLP tasks will be performed, those embeddings will surely outperform the embeddings which are obtained from the common corpus or publicly available embeddings for the Bengali language. So, for a task like News Category Classification or Sentiment Analysis if word embedding can be created from a domain-related News Corpus or Sentiment related corpus it will perform way better. Nowadays, labeled data sets are hard to come by. This work demonstrated how unsupervised approaches, such as clustering, may be used effectively and produce excellent outcomes, such as news category clustering. This work recorded the performance of our embeddings in several scenarios, such as News Category Classification and Document Clustering tasks. Word Embedding created from a domain-related corpus shows promising results over a common corpus like Wikipedia dump corpus.
IoT and Data Mining Techniques to Detect and Regulating of Solar Power System

As a result of its practical and long-term benefits, renewable energy sources are the subject of intensive study. For instance, solar power stations are among the renewable energy systems that may be employed in various places due to their cheaper installation cost and maintenance requirements compared to traditional methods, despite their smaller footprint. Most small power plants solve the problem of equipment sprawl by setting up shops on an outside terrace. On the other hand, big power plants need vast land to construct. Maintaining such a big power station area is difficult for human workers. The suggested method utilizes the Internet of Things (loT) and data mining approaches to assist human managers in identifying power outages and faulty sections of solar power installations, expediting the process of correcting faults, and boosting the production plant's effectiveness.
Revolutionizing Tender Management through Blockchain Technology

Contracts or tenders are frequently used by both private and public companies to purchase merchandise or services. Traditional paper-based systems are often prone to errors, delays, and corruption. However, poor management of tenders can resultin significant losses due to practices such as contractor favoritism, inadequate record-keeping, lack of transparency, hacking, and data manipulation. To address this problem, we have implemented a secure and straightforward blockchain technology along with encryption and a foolproof block-based transaction management system. By using blockchain technology, we aim to ensure complete transparency in the bidding process and safeguard all transaction-based data, including proposals, company-profiles, applications tender documents, approval officer details, performance records and rejection details. The primary objective of this project is to guarantee the integrity of the entire tender management process and to guarantee fairness and transparency in the procurement process, and simultaneously increase efficiency and lower expenses. However the biggest challenge is the potential for security breaches, and the need for regular audits and updates to the system which can be possible by advanced security systems.
Node based Label Propagation for Bitcoin Transaction Pattern Identification Over Similar Community

People have conducted decentralized transfer transactions through Bitcoin addresses ever since the Bitcoin system launched online, drastically improving the convenience of transactions. Simultaneously, peer-to-peer transaction logs have become a subject of study. However, it requires a significant amount of time and computing capacity to examine the entire network directly, and it is not conducive to observing the transaction mode within the entity. Consequently, it is possible to construct and analyses the transaction network based on the entity service community and further investigate entity behavior and Bitcoin entities within the service community. A central node-based label propagation algorithm is proposed by enhancing the conventional label propagation algorithm, which divides the Bitcoin entity transaction network into districts and analyses the core communities, including exchanges and mining pools. The transaction method is realistic and straightforward to comprehend. The enhanced label propagation algorithm can converge more quickly and reduce the randomness of the results of community division. The experimental results demonstrate that the internal transaction modalities of various services vary. The simplified display makes the Bitcoin transaction network more readable.