Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence Technology in the Event Analysis of Substation Monitoring Information

In order to solve the problems of centralized monitoring of multi-site and multi-equipment in substations, and the low efficiency of information processing, this paper puts forward the application method of artificial intelligence technology in event analysis of substation monitoring information. Firstly, the paper analyzes the current situation, trends, and hot spots of substation intelligence, and then introduces the design and application of artificial intelligence monitoring information event architecture. An improved BP neural network algorithm is designed, which combines the improved BP neural network algorithm with L-M fast learning method to realize the event processing of substation equipment information and uses image processing technology to scan the event monitoring results with artificial intelligence vision. By taking real-time photos of the monitoring center, key information is extracted, and different event results are output and displayed, which helps the staff to obtain information quickly.
MINERALOGICAL FEATURES OF GOLD ORE MANIFESTATIONS OF THE SOLOLI UPLIFT OF THE OLENEK ARCH

The paper deals with studied mineralogical and geochemical features of gold-sulphide manifestations of the Sololi uplift of the Olenek arch (northeast of the Siberian Platform) in order to identify their mineralogical features and prospects for their further research. The identical mineral composition of gold-sulphide formations developed by Early Proterozoic rocks of the Eekitian series and rhyolites of the same age gave reason to assume the simultaneity of their formation and to refer them to the general stage of ore formation. According to the established mineral paragenesis, described gold sulphide manifestations are attributed to the gold-quartzsulphide formation type. The discovery of gold sulphide mineralization in the zone of tectonic fracturing of Early-Middle Permian sandstones indicates the formation of ore occurrences in the area associated with the processes of tectonomagmatic activation of the Mesozoic. The identification of indicators of low-temperature hydrothermal processes as mineral paragenesis – mercury-containing gold, cinnabar and barite, testifies to shallow and low-temperature conditions for the formation of this ore manifestation. It was found that according to the Co/Ni ratio, 28% of pyrites (including gold-bearing ones) are associated with hydrothermal processes. In general, the detection of native gold in quartz veins in free form and as microinclusions and impurities in pyrite, as well as the discovery of relatively large poorly rounded attachments of gold to quartz in stream flows dewatering the studied ore formations, allows researchers to positively assess the prospects for further study of ore manifestations of the Sololi uplift of the Olenek arch.
Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.
Explainable Artificial Intelligence (XAI) for IoT

Artificial Intelligence and Machine Learning are the latest topics across industries. A lot of concentration has been given to these areas and still the adoption has been challenged by users and experts in this field in the search for some kind of solution to be provided that the output can be trusted by all. The purpose of this paper is to focus on the sensor data coming from various IoT devices and how the data can be interpreted by various available algorithms. The ML algorithm is considered a black box with a focus on providing the required output without finding the causes behind the decision and working mechanism provided by that model. In this chapter, we tried to explain various common techniques/models available for eXplainable Artificial Intelligence (XAI) and how those can be used for IoT data.
Self-adaptive attention fusion for multimodal aspect-based sentiment analysis

Multimodal aspect term extraction (MATE) and multimodal aspect-oriented sentiment classification (MASC) are two crucial subtasks in multimodal sentiment analysis. The use of pretrained generative models has attracted increasing attention in aspect-based sentiment analysis (ABSA). However, the inherent semantic gap between textual and visual modalities poses a challenge in transferring text-based generative pretraining models to image-text multimodal sentiment analysis tasks. To tackle this issue, this paper proposes a self-adaptive cross-modal attention fusion architecture for joint multimodal aspect-based sentiment analysis (JMABSA), which is a generative model based on an image-text selective fusion mechanism that aims to bridge the semantic gap between text and image representations and adaptively transfer a textual-based pretraining model to the multimodal JMASA task. We conducted extensive experiments on two benchmark datasets, and the experimental results show that our model significantly outperforms other state of the art approaches by a significant margin.
Analysis of phase calculation error introduced by the extinction ratio in instantaneous phase shifting interference

Instantaneous phase shifting interferometry technology, the core component of which is the pixel micropolarizer camera, has been widely used in commercial interferometers. This technology has the superiority of single-frame acquisition, vibration insensitivity, and no need for phase shifting devices. However, due to manufacturing defects and accuracy limitations, the extinction ratios (ER) of the micropolarizer array are different and fairly small, directly affecting the phase calculation accuracy. This paper initially derives a theoretical expression for the phase calculation error introduced by the extinction ratio (ER) and proposes the error correction model to reduce phase calculation errors caused by the extinction ratio. The theoretical analysis can serve as an important basis for accurately assessing the polarization characteristics of a pixel micropolarizer camera. Quantifying the impact of the extinction ratios provides significant support for the selection of polarization equipment. In addition, the paper proposes a calibration model to improve measurement accuracy, which can serve as an effective means to reduce the impact of the extinction ratio (ER). The innovative research content revealed the influence of extinction ratio (ER), serving as a valuable complement to the existing analysis and research on extinction ratio (ER).
Analysis of Learnability of a Novel Hybrid Quantum-Classical Convolutional Neural Network in Image Classification

Abstract We design a new hybrid quantum-classical convolutional neural network (HQCCNN) model based on parameter quantum circuits. In this model, we use parameterized quantum circuits (PQC) to redesign the convolutional layer in classical convolutional neural networks (CNN), forming a new quantum convolutional layer to achieve unitary transformation of quantum states, enabling the model to more accurately extract hidden information from images. At the same time, we combine the classical fully connected layer with PQC to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification. Finally, we used the MNIST dataset to test the potential of HQCCNN. The results indicate that HQCCNN has good performance in solving classification problems. In binary classification tasks, the classification accuracy of numbers 5 and 7 is as high as 99.71%. And in multivariate classification, the accuracy rate also reached 98.51%. Finally, we compare the performance of HQCCNN with other models and find that HQCCNN has better classification performance and convergence speed.
Sistem Pakar Diagnosis Penyakit Kulit Pada Manusia Dengan Metode Naïve Bayes Menggunakan Shiny

Skin is the outermost part of the body's organs which is very susceptible to being influenced by the environment. Besides environmental factors, behavior and lifestyle could also be the cause of skin diseases. An expert system for diagnosing skin diseases can help patients determine the diagnosis of the disease they are suffering from based on the symptoms they feel more efficiently. This system can also provide early detection to patients so that the disease suffered by them can be treated quickly. This research develops a website-based expert system for diagnosing skin diseases in humans using the Naïve Bayes Classifier (NBC) method. The expert system in this research was developed using Shiny Package in R Programming with 10 types of disease and 26 symptoms data. The results of this research show that the expert system has been successful in diagnosing human skin diseases.
Research on Application of Identity Authentication Encryption Technology in Blockchain Scene

With the wide application of computer network and intelligent terminal, the application of blockchain technology is rapidly popularized, and the corresponding information security issues become increasingly important. This paper introduces the data encryption standard DES of symmetric encryption algorithm, and discusses the public-key infrastructure system PKI. It is a third-party platform to ensure the security of information during transmission, mainly responsible for issuing certificates with digital signature of CA center and managing public and private-keys needed in RSA algorithm. Several asymmetric encryption algorithms are studied, focusing on RSA asymmetric encryption algorithm, and the method of encrypting public-key decryption with private-key is designed and implemented to solve the problem of information authenticity identification, i.e., identity authentication namely identity authentication. RSA public-key generation, information encryption and decryption are designed, and RSA key generator module, encryption module and decryption module are realized. The results show that RSA algorithm is feasible and safe in blockchain scene.
Multi-view Contrastive Enhanced Heterogeneous Graph Structure Learning

PDF HTML XML Export Cite reminder Multi-view Contrastive Enhanced Heterogeneous Graph Structure Learning DOI: 10.21655/ijsi.1673-7288.00307 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:As a learning method of heterogeneous graph representation, heterogeneous graph neural networks can effectively extract complex structural and semantic information from heterogeneous graphs, and perform excellently in node classification and link prediction tasks to provide strong support for the representation and analysis of knowledge graphs. Due to the existence of some noisy interactions or missing interactions in the heterogeneous graphs, the heterogeneous graph neural network incorporates erroneous neighbor features, thus affecting the overall performance of the model. To solve the above problems, in this paper we proposes a heterogeneous graph structure learning model enhanced by multi-view contrast. Firstly, the semantic information in the heterogeneous graph is maintained by the meta-path, and the similarity graph is generated by calculating the feature similarity among the nodes under each meta-path, which is fused with the meta-path graph to optimize the graph structure. By contrasting the similarity graph and meta-path graph as multiple views, the graph structure is optimized without supervision information, and the dependence on supervision signals is eliminated. Finally, for addressing the problem that the learning ability of the neural network model is insufficient at the initial training stage and there are often erroneous interactions in the generated graph structure, we design a progressive graph structure fusion method. Through incremental weighted addition of meta-path graphs and similarity graphs, the weight of similarity graphs in the fusion is changed. This not only prevents erroneous interactions from being introduced in the initial training stage but also achieves the purpose of employing the interactions in similarity graphs to suppress interference interactions or complete missing interactions, which leads to the optimized heterogeneous structure. Meanwhile, node classification and node clustering are selected as the verification tasks of graph structure learning. The experimental results on four real heterogeneous graph datasets prove that the proposed learning method is feasible and effective. Compared with the optimal comparison model, the performance of this model has been significantly improved under both tasks. Reference Related Cited by
FS-Net: Frequency Statistical Network for Temporal Knowledge Graph Reasoning

PDF HTML XML Export Cite reminder FS-Net: Frequency Statistical Network for Temporal Knowledge Graph Reasoning DOI: 10.21655/ijsi.1673-7288.00308 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Temporal Knowledge Graph (TKG) reasoning has attracted wide attention of researchers. Existing TKG reasoning methods have made great progress through modeling historical information. However, the time variability and unseen entities (relations) are still two major challenges that hinder the further improvement of this field. Moreover, since the structural information and temporal dependencies of the historical subgraph sequence have to be modeled, the traditional embedding-based methods often have high time consumption in the training and predicting processes, which greatly limits the application of the reasoning model in real-world scenarios. To address these issues, in this paper we propose a frequency statistical network for TKG reasoning, namely FS-Net. On the one hand, FS-Net continuously generates time-varying scores for the predictions at the changing timestamps based on the latest short-term historical fact frequency statistics; on the other hand, based on the fact frequency statistics at the current timestamp, FS-Net supplements the historical unseen entities (relations) for the predictions; in particular, FS-Net does not need training and has a very high time efficiency. Plenty of experiments on two TKG benchmark datasets demonstrate that FS-Net outperforms the baseline models. Reference Related Cited by
The Mediating Effect of ICT on the Relationship Between Innovative Capacity and Student Satisfaction

The study aimed to explore the relationship between innovation capacity and student satisfaction at universities in Lambayeque, Peru, considering the mediating role of Information and Communication Technologies (ICT). A non-experimental, cross-sectional quantitative methodology was employed, gathering data from 315 students using a Likert-type questionnaire. Reliability and validity analyses showed good internal consistency and convergent validity of the measured constructs. The results indicated that innovation capacity positively affects student satisfaction, both directly and through ICT. The model explained 68.5% of the variability in student satisfaction and 42.3% in the use of ICT, with discriminant validity between innovation capacity and ICT that might require review. The research confirmed a significant relationship between innovation and student satisfaction, mediated by ICT, highlighting the importance of integrating these technologies into the educational process to enhance innovation and improve the educational experience of students.
Agricultural Financing, Food Production and Poverty Reduction in Nigeria: Evidence from ARDL Model (Note 1)

The need for adequate food production in Nigeria is increasingly becoming a must-achieved objective for policy makers and government agencies owing to continuous rise of food prices. Notwithstanding this, food production requires adequate financing and by extension, plays some important role in reducing poverty level. Hence, this study investigates the extent to which specific agricultural financing will impact food production and reduce poverty level. The study employs secondary data which spans 1981 to 2021 and adopts Autoregressive Distributed Lag (ARDL) for the methodology. The choice of ARDL is to capture the impact for short- and long-run period. The findings from the study highlight the following: specific agricultural financing is found to be highly instrumental in promoting food production in the country. Aside this, food production is equally found to have much impact in reducing poverty level. This impact is assured in both short- and long-run periods. However, while available arable land significantly influences agricultural finance, it is much found to be sustained and long lived in the long run. Thus, the policy implication arising from this study gives necessity to a policy that will enhance credit facilities to the farmers and other supports that will ensure food surplus.
Strategi Pengembangan Keterampilan Teknologi Informasi Bagi Siswa SMK Terpadu ALTA Kabupaten Semarang

The development of knowledge in information technology significantly influences student learning, especially in entrepreneurship subjects, thus making various digital-based learning methods develop rapidly. Starting from the idea of a team of University of Science and Computer Technology (STEKOM) lecturers providing training to Alta Integrated Vocational High School students in Semarang Regency, which aims to increase competence in the field of information technology. The material is related to branding, google my business and google trends. Lecture methods and direct practice are used in this training activity with the concept of community service. The stages of this training consist of Registration, Activity, Output, and Evaluation. It is hoped that in the future this kind of activity can be held regularly and continuously.
Effect of Digital Marketing Channels on Consumer Buying Intentions in FMCG Industry in Pakistan

Digital marketing and Advertising are pivotal in increasing customer information and awareness towards products and services and engaging them in decision-making. Digital mediums are persuasive advertising mediums, and due to the advent of technology and innovation, digital marketing and advertising are important. For instance, YouTube is used as a persuasive medium for advertising and shapes consumer decisions toward purchase. This research is based on the post-positivism research paradigm, and it is quantitative. For this purpose, the researcher used the questionnaires to collect data and performed hypothesis testing. The research was conducted based on FMCG product purchases and data collected from consumers of FMCG brands who are watching Digital advertising and are involved in purchase decisions. The data was collected through Google survey forms from the 385 respondents based on convenience sampling. The collected data was processed through Smart PLS and reliability tests, correlation analysis and regression analysis. The results were analyzed based on reliability; the results are consistent, and more than 70% accuracy was found in data collection. All the variables, including Digital Advertising, use of social media, Content Marketing, Product appeal, and Digital Display, have a significant impact on mediating variable customer satisfaction and interaction and this significant impact on consumer buying intentions. One hypothesis is rejected as content marketing in this case in significant variables, and all variables are significant with mediating and moderating impacts on consumer buying decisions.
Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites

Factors Affecting Respondents’ Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites
PRODUTO AUDIOVISUAL EDUCATIVO PARA ADOLESCENTES: APRENDER DIFERENTE SOBRE IMAGEM CORPORAL E TRANSTORNOS ALIMENTARES

Electronic screens are very present in the juvenile routine. Virtual language has become an essential platform, with social media being a conducive environment for education. The main types of eating disorders are anorexia, bulimia, binge eating, and vigorexia. Thus, the objective was to produce educational materials in the form of videos for nutritional education on the relationship between body image and eating disorders for adolescents. This is a qualitative developmental study carried in private university, from August to September 2022. The scientific literature on the subject was selected from the information bases: Scielo, Pubmed, Google Scholar, and Ministry of Health from 2018 to 2022. The manufactured products were aimed at literate adolescents, from TikTok® app based on the summary/file of scientific information. Five short videos (up to 60 seconds each) were created in the Videoscribe® application through animated illustrations and narration. Video 1–“What are eating disorders”; Video 2–“Adolescence”; Video 3–“The main types of eating disorders”; Video 4–“Binge eating and vigorexia” and Video 5–“Treatment and healthy eating”. Thus, videos with nutritional education content for the knowledge of the relationship between body image and eating disorders aimed at adolescents seem to be important recreational resources for health education, and can also be validated for effectiveness and scientific proof of their effect on the public.
Penerapan Data Mining Dalam Menganalisis Pola Belanja Konsumen Menggunakan Market Basket Analysis

Currently, almost every activity is related to data. in the business sector, daily sales transaction data stored in the database system will always increase and accumulate. The existing data is only used as an archive by the shop owner so that it has an impact on sales strategies that are not implemented well, even though the existing data can be processed into information to determine the layout of goods so that it has an impact on increasing the occurrence of impulse buying, increasing or maintaining turnover, and minimizing product waste. accumulate until it expires which can be detrimental to the shop.The aim of this research is to find consumer shopping patterns using Marker Basket Analysis. This research method is called market basket analysis or also called association rules, which is a data mining technique for finding patterns that often appear simultaneously in transaction data, so that it can be used as a method for finding information about what kinds of goods are frequently used. purchased by consumers simultaneously. The results of this research, based on data analysis using the Rapidminer application, found 25 associative relationships or rules with a lift ratio value of more than 1, these rules become a reference in determining the layout of goods. Providing recommendations for layout changes aims to make it easier for consumers to shop, increase the possibility of impulse buying by consumers, and maximize product display, thereby reducing the accumulation of goods in the Purnama Store Warehouse.