Reviewerly/exHarmony huggingface dataset:
IoT Enabled Wearable Technology Jacket for Tracking Patient Health and Safety System

Over the years, there have been ongoing efforts to use clothing as a vehicle for distributing digital capabilities. Information technology (IT) is being incorporated into garments to enable people to work more quickly. Because of their increasing availability, accessibility, and, most significantly, cost, wireless body area networks (WBANs) will undoubtedly be used in healthcare to enhance medical data utilization. Whether it has to do with post-operative recovery or Monitoring, applications for their function in the medical field are already being looked into. Wearable technology allows users to complete activities more efficiently by enhancing one or more of their senses with artificial information. The system proposes a method that displays critical information for doctors on a particular cloud for data analysis. In this concept, sensors are attached to someone's coat to collect real-time data. This data is analyzed, uploaded to the cloud using IoT Medium, and notified in the case of an alarming condition. The data access to real-time data, a website built into this module, and an alert notification system to warn the hospital and the family if the numbers are abnormal. A live stream is recorded using the camera, while the GPS module tracks the location. The doctor can take the proper step based on the patient's health.
Recommender Systems for NFT Purchasing

With technological advancements, both habits and needs undergo significant changes. Moreover, these changes introduce numerous innovations into our lives. This study examines the application of recommendation systems to one such innovation: NFTs. Firstly, the structure and characteristics of NFTs operating on the blockchain are explored. Secondly, an original dataset suitable for an NFT recommendation system is developed. Using this dataset, fundamental recommendation system methods such as content-based and collaborative filtering are applied. The results obtained are then evaluated. Based on these findings, considerations for implementing an NFT recommendation system and potential directions for future research are discussed.
Construction of Reliability Evaluation Model of Cigarette Quality Supervision Measuring Instrument Based on Random Forest Algorithm

Abstract This paper combines the cigarette quality supervision measurement project, summarizes the key instruments for cigarette quality supervision, constructs the reliability evaluation model of cigarette quality supervision measurement instruments, adopts the random forest method and carries out model optimization so as to solve the weights of quality evaluation indexes of cigarette quality supervision measurement instruments. The QTM comprehensive test bench is selected to measure the quality and circumference of the traceability comparison stick of cigarette physical inspection equipment and combined with the Random Forest optimization algorithm to analyze the impact of measurement repeatability on the moisture content of cigarettes. Using the normalized deviation evaluation method, we can compare the consistency of the detection results of the evaluation model of this paper with the detection results of the parent company. It is analyzed that the maximum permissible error of cigarette circumference is ±0.05, and the maximum permissible deviation of comparison D max is ±0.06. The circumference deviation of sample A is 0.04&lt;0.06, the sample is qualified, and it is consistent with the testing results of the parent company.
Bibliographical Subjects in the Scientific Work of Central Libraries of the Constituent Entities of the Russian Federation

Bibliography is one of the main areas in the range of scientific specialties of the Higher Attestation Commission in section 5.10 “Art History and Cultural Studies”, specialty 5.4.10 “Library Science, Bibliography and Bibliology”. The article is devoted to the problem of reflecting bibliographic subjects in the scientific activities of the central libraries of the constituent entities of the Russian Federation. The purpose of the article is to consider the problems of creating bibliographic resources, conducting applied scientific research on bibliographic topics and how trends in the development of bibliographic activities of libraries are presented in the publications of specialists. The author concludes that not only conducting bibliographic research itself, but also creating bibliographic resources of various types and genres is scientific work, and libraries rightfully account for their scientific activities by their creation. By carrying out activities to create bibliographic resources, libraries solve historical and cultural problems: preserving the cultural, historical and scientific heritage of the peoples of Russia through bibliography. Research activities to study systems for the formation of bibliographic resources in libraries of different types and for bibliographic services to different categories of users are actually bibliographic studies, they are aimed at improving bibliographic activities themselves. By conducting such research, bibliographers determine the prospects for the development of their activities.
Application of the Certainty Factor Method for Mobile-Based Identification of Freshwater Fish Diseases

Freshwater fish its the food sources with profitable sales, consistent care can achieve the maintenance process until successful. Freshwater fish that have a high selling value include Gurami and Catfish. Gurami fish sales per seedling Rp. 3,000 and Lele fish sales start from Rp. 1000 to Rp. 0.500. Fish care needs to be considered to maintain its quality. Various problems are also experienced by farmers. The slow identification process makes fish affected by the disease faster and loses. Several factors inhibit the fish is of illegal drugs, overfeed, and environmental unhygiene conditions. The role of experts that is not sufficiently available makes limited information about controlling fish diseases so that it takes time and costs little. Utilizing technological progress, expert systems can help to solve problems that previously could only be solved by an expert. The Forward Chaining Method is a method on an expert system that is able to do a reasoning with advanced forward techniques. The Certainty Factor method is a method that can give a degree of confidence in a disease. An expert system built to identify freshwater fish diseases was built into the deep android operating system by the Waterfall development method. Data taken includes as many as 10 symptom data and 29 disease data implemented into the system. With this expert system in place, it aims to provide benefits to facilitate access in adding information about fish diseases so that countermeasures become faster
Model E-Asesmen Berbasis Aplikasi pada Sekolah Menengah Atas di Era Digital: Systematic Literature Review

Education in today's digital era is required to be adaptive and accommodating to the development of information and communication technology. The education process is carried out dynamically according to civilisation. One of the significant changes that occur today is the transfer of assessment practices from conventional-traditional to electronic assessment practices which are then called E-Assessment. This electronic assessment is developed with various variants, starting from web-based, applications, and so on. Internet access has been massive and widespread throughout the region, making most people assume that E-assessment is relevant to be applied in today's learning world. This research is a type of qualitative research with a systematic literature review (SLR) approach. Data sources come from research journal articles and books that are in accordance with the theme of the discussion. The data analysis technique uses content analysis. The findings or results of the study are that E-assessment is relevant to be implemented in senior high schools because of their skills in accessing and operating various applications. The digital literacy possessed by today's millennial generation greatly supports the E-assessment process in schools. Application-based E-Assessment can be developed with several applications, such as kahoot, quizizz, moodle, and others. The application of E-assessment has several advantages, such as saving money, reducing time for correction, feedback and evaluation results can be received in real time, and improving the digital skills of educators and students
Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and Machine Learning

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. This work proposes an emotional image retrieval strategy for multimodal emotion detection. This strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. This is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. The preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy.
Exploring the Trade-Offs Between Blackbox and Explainable AI: A Comparative Study

Healthcare, finance, and transportation are just a few of the sectors where artificial intelligence (AI) is now a necessity. AI algorithms come in two flavors: explainable and blackbox. Explainable models are intended to be more transparent and interpretable than blackbox models, which are complicated and challenging to understand. Through a comparative analysis, this article investigates the trade-offs between explainable AI and blackbox AI. It specifically looks into how explainability affects accuracy, user perception of AI models, the efficiency of explainability methods, domain-specific effects, and ethical ramifications. The study seeks to offer useful insights into the advantages and disadvantages of these models and to assist in guiding decisions regarding the use of explainable and blackbox AI in various applications.
On Chatbots for Visual Exploratory Data Analysis

Analyzing data and creating effective visualizations often requires extensive domain expertise. For users with less experience, it can be difficult to know how to get started with exploratory data analysis (EDA) and how to approach the code. Chatbots can reduce the gap between analysis outcomes and user expectations by leveraging multi-turn conversations to provide a more natural interface between the user and computer-agent. To inform the design of future visual EDA chatbots, we conduct a survey and interview study with ten potential users. Our results suggest that users want a visual EDA chatbot that can make exploratory data analysis easier, while also augmenting their knowledge of visualization and analysis techniques. Between the initial survey and post-interview questionnaire, we saw increased optimism overall for the usefulness and anticipated analytic ease of visual EDA chatbots. Based on these results, we identify four key design guidelines: future visual EDA chatbots should (1) understand the user’s data and intent, (2) respond with useful visualizations, (3) leverage the history of the visualizations and data, and (4) produce verifiable and shareable analysis processes.
Perceptual Video Summarization using Keyframes Extraction Technique

In recent years, there has been a sudden rise in the amount of time people spend watching videos both online and offline, proving that video is the most dependable source of information. To fully grasp the context, a person must watch the entire video, which presents a significant barrier to information extraction from videos. Managing one's time has become an issue due to the constant development of digital multimedia. It has been the focus of numerous studies to determine the best way to handle such a massive amount of data or lengthier videos. Video summarization (VS) is an essential study topic for solving the problem. The most important part of the VS process is the key frame extraction. The main principle of key frame extraction is to reduce the huge number of individual video frames to a manageable number of crucial frames. In this research, the standard data will be collected and processed via frame extraction and uniform sampling. To complete VS, the processed data went through a procedure of feature extraction and clustering. For feature extraction, we use the Convolutional Neural Network (CNN) and Video Summarization (VSUMM). Next, K-Means and Gaussian are two popular methods for cluster analysis. The optimum feature extraction and clustering approach is determined by analysing the performance metrics. According to the results of the experiments, the CNN with K-Means yields the lowest PSNR, at a value of 6.45, and the highest F-Measure of 67.64%.
Incremental Recommendation Algorithms Based on Word Embedding Model and Neural Networks

Traditional collaborative filtering recommender systems need to retrain the entire dataset in case of drastic data changes, which generates huge computational overhead in big data scenarios. In view of this,an incremental collaborative filtering recommendation system based on sequence similarity measurement and neural networks was proposed. In the offline preprocessing phase, the user profile, explicit feedback information and implicit feedback information were modeled as sequence formatted contexts, and a sequence-based similarity metric is designed; the word embedding model and the collaborative filtering system are combined to predict the list of items favored by the user using the context. This result for that experiment reflects that the recommendation results of the algorithm exhibit better alignment satisfaction, novelty and diversity, and it supports incremental system update processing as well.
Federated Learning with Class-Imbalanced Heterogeneous

Federated learning is a distributed machine learning framework that allows multiple clients to collaborate in training a global model. In federated learning, many clients can train together and make full use of information resources. However, in practical scenarios, the data owned by different users are not from the same distribution. The integral data is heterogeneous, which will slow down the training speed of federated learning and reduce the performance of the model. And in this paper, we focus on addressing label heterogeneity and propose a clustering federated learning framework that utilizes the flow of solution procedure (FSP) matrix. And we set up a small amount of public dataset at the central server. The client participating in the training generates FSP matrix on this dataset. The central server clusters the clients into several groups according to these matrixes. The clients clustered into the same group are considered to have more similar data distributions. We also conduct quantities of experiments on CIFAR10 and MNIST datasets to verify that our method performs better than the traditional federated learning and some other algorithms in terms of performance and has good stability. It can function properly in various scenarios with non-independent and non-identically distributed data.
An Image Scrambling Encryption Algorithm Based on Combinatorial Chaos Mapping

With the development and application of network technology, digital images, as a form of information transmission, are at high risk of being stolen or decrypted. Our scheme utilizes the nature of low dimensional chaos to generate a high dimensional chaotic system with more variables and higher complexity by mixing two different low dimensional chaotic systems to protect digital images and confirms that it outperforms the original system through simulation experiments. In this paper, a new image scrambling encryption algorithm for multi chaotic system is designed by combining improved tent mapping and high-dimensional chaotic system, and compared with the existing encryption scheme in terms of simulation experiments and security performance parameters, which proves that the scheme has better comprehensive ability.
Design of Computer Network Security Storage System Based on Digital Cloud Computing Technology

The design of a computer network security (CNS) storage system based on digital cloud computing technology (CCT) is a solution proposed to address the increasing network security threats. With the widespread application of computer networks in various industries, data security has become an important concern. Traditional storage systems often cannot meet the requirements for data security and reliability, so a new design is needed to protect network data. This storage system based on digital CCT has multiple functions. Firstly, it can encrypt, backup, and restore data to ensure that it is not accessed, tampered with, or lost without authorization. Secondly, the system ensures that only authorized users can access and modify data through authentication and access rights management, thereby preventing unauthorized access. In addition, the system can also record the access and operation of data, making it easy to trace and detect whether there are security vulnerabilities or violations in the later stage. Storage systems based on CCT also have the characteristics of elasticity and scalability. It can flexibly expand storage space according to user needs and provide high availability services. Through experiments, it can be seen that the CNS storage system using digital CCT has only one successful intrusion, data loss, and running error within one minute, which is a significant improvement compared to traditional network security storage systems. Finally, the design of a CNS storage system based on digital CCT can also optimize the use of network and storage resources, improve the efficiency of data transmission and processing, and provide a better user experience. In short, this system can effectively protect the security and confidentiality of data, provide reliable storage and access services, and provide better data protection and security defense capabilities for network users.
A Collective Approach to SMS Spam Detection: Harnessing the Power of Multimodal Features, Machine Learning, and Advanced Classifiers

In the current of mobile communication, Short Message Service (SMS) remains a dominant means of interaction despite the proliferation of messaging apps. Unfortunately, this increase in SMS traffic has given rise to an increase in SMS spam. This unwelcome phenomenon has become a conduit for advertising and fraudulent activities, causing significant inconvenience and harm to users. Thus, achieving a better way of SMS spam filtering is a formidable challenge. In this research, a machine learning based approach to spam SMS detection is used. This approach uses a variety of machine learning algorithms, including Logistic Regression, Support Vector Machines (SVMs), Multinomial Naive Bayes (MNB), Voting Classifiers, Stacking Classifiers and many other ML models. The best model based on its performance, measured by accuracy and precision is found out to be Multinomial Naïve Bayes, this model is chosen and further used for spam and ham classification. This model resulted in accuracy of 97% and precision of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> . This shows that multinomial naïve bayes is the most suitable model for this classification task.
Secure Auditing with Encrypted Trace Logs in Multi-Cloud Storage Management

Cloud computing revolutionizes the way resources are utilized, offering a platform where they can be dynamically allocated based on demand. This on-the-fly data processing and storage capability has led various providers to offer both free and premium storage solutions. However, with a multitude of providers in the market, accessing data scattered across them can be cumbersome. Hence, the emergence of multi-cloud storage management tools, which provide a unified interface for users to seamlessly access data without juggling between multiple providers. Though authenticated access safeguards data, maintaining its integrity requires oversight of user activities. This calls for the implementation of a Trace Log that meticulously records each action. Such logs, while crucial for auditing, are sensitive and can be vulnerable. To bolster their security, encryption techniques can be implemented, using unique user-specific keys. This not only enhances the log's security but also ensures controlled, authenticated access. A proposal for a robust, secure, and efficient system to manage and monitor data across multiple cloud platforms.
Large Scale Multiplex Latent Graphs for Recommendation in Baidu

In commercial systems, the interaction between user behavior and advertising content forms a complex graph network that exhibits multi-scenario and multi-modal characteristics. Multi-scenario refers to the fact that ads are typically displayed in different scenarios on the same platform, such as Baidu App’s news feed and short video scenes. Across different scenarios, users’ interests have both commonalities and differences. Multi-modal refers to the fact that the influence of advertising content modality on users varies, and users’ sensitivity to different content modalities may also differ. We believe that the fundamental relationship between user behavior and advertising content may be multi-layered, influenced by the types of presenting content scenarios and the unique features of each modality. For example, in the news feed scenario, the importance of video titles may be different from that in the video stream scenario, where the visual content of the video may be more prominent. We propose a novel heterogeneous graph neural network method that integrates multi-scenario domains and multi-modal content features, which we call the Domain-Aware Multi-Modal graph model (DAMM-GCN). Our model achieves significant improvements in multiple scenarios compared with the traditional flat graph models such as Metapath2vec [1], GraphSAGE [2] and MMGCN [3]. Furthermore, our model has been deployed in the Baidu advertising system, obtaining 2.08% improvement on CPM (Cost Per Mille).
Implementing Machine Learning Strategies to Evaluate Analytical Algorithm for Unsupervised Narratives Synthesis

As electronic records overwhelm across the web these days, the content extraction field is gaining prominence and is being employed extensively. Narratives pieces, which might be summarized in an extracting manner devoid losing their deeper significance, are a key electronic material utilized by individuals taking competitive assessments. We are attempting to create a method that might help learners in extracting current information from narratives in a shorter length of time. The contrasting analysis among two modern and sophisticated procedures is accomplished in order to highlight the superior one. The extractive summarization is carried out utilizing three distinct Text Ranking algorithm procedures, summaries functioning, proportion, and word count. This study focuses on the analysis of the outcomes that were obtained with the utilization of extractive and unsupervised summarization. The aim of the current work is the analysis of outcomes obtained through the Text-Rank methodology utilizing the Gensim Environment with the ROUGE approach.