Reviewerly/exHarmony huggingface dataset:
Ensemble Learning Based Social Engineering Fraud Detection Module for Cryptocurrency Transactions

The practical applications of blockchains can far supersede the widely known trading and cryptocurrency realm. If any service provider is looking for a consistent, immutable, and multitenancy-supported ledger, then blockchain is the promising solution. Nowadays, social engineering attacks are prevalent. And the attackers deceive cryptocurrency traders. This work investigates various ensemble learning, neural network, and machine learning algorithms for fraud detection and identifies the best decision-making algorithm. It is observed that Adaptive Boosting (AdaBoost) algorithm outperforms with an accuracy of 98.92%. Further, the fraud detection module is integrated with an application developed for cryptocurrency transactions. Before a new transaction is committed to blockchain, The fraud detection module intervenes and alerts the user. We have also designed a test bed of deployable Peer-to-Peer (P2P) network to simulate cryptocurrency transaction.
Sentiment Analysis Using Lexical Approach and Fuzzy Logic

Understanding the feelings expressed in a statement is the goal of sentiment analysis. Depending on the thoughts expressed, a statement may be good, neutral, or negative. Positive, neutral, or negative feelings might be present in a statement. The reality is that the feeling expressed in each sentence whether it be positive, negative, or neutral is not always obvious. The examination of comparison, negation, intense, and sarcastic phrases, as well as the difficult work of dealing with grammatical errors, provide enormous hurdles to this process. The SA system is built in this study using a hybrid technique that combines dictionary-based approaches with fuzzy logic to address these issues. The suggested system’s outputs may be classified as either high positive, positive, neutral, negative, or high negative emotions. The analysis of customer satisfaction tweets about cloud services from Google, Amazon, and Microsoft revealed that the suggested method performs significantly, with results of 85.6% precision, 88.4% recall, and an 83.6% F-score.
Private Storage Cloud for Facilitate the Functions of Organizations

Cloud storage or cloud computing is a set of technologies and equipment services that are capable of giving remote storage service, within minutes, and the ability to retrieve them easily [1]. Since the paper storage of files faces problems of damage and loss of documents in addition to time and effort in case some files are requested and sent to another section. To solve this problem, we suggested creating a private cloud for any organization or department. The work included creating a storage cloud that includes file archiving and the possibility of facilitating interaction between college staff in terms of exchanging files and inquiring about them without trouble and taking a long period of time for research and adding the nature of privacy to inform who is not concerned with this or without college staff and this is done by making accounts for college cadres so that they can access the cloud, starting from the Deanship, associates, scientific departments and administrative departments, and each department has its own files that cannot be accessed.
The Analysis of Cyber security in Intelligent Transportation Systems Using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Method

Cyber security has become a critical concern in today's interconnected world, with the escalating frequency and sophistication of cyber threats. To effectively protect digital assets and sensitive information, organizations must adopt robust cybersecurity systems. The Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method has emerged as a promising approach for evaluating and improving cybersecurity systems.This research presents an innovative application of the MOORA method to enhance cybersecurity systems. The MOORA method is a Multi-Criteria Decision Analysis (MCDA) technique that enables decision-makers to rank alternatives based on multiple criteria, ultimately aiding in selecting the most suitable solution. In the context of cybersecurity, various evaluation criteria are considered, such as threat detection accuracy, incident response time, scalability, resource utilization, and cost-effectiveness.Through the integration of the MOORA method, this study offers a systematic and quantitative assessment of cybersecurity systems, addressing the limitations of traditional evaluation techniques that often overlook the complexity of cyber threats. By prioritizing the criteria most relevant to an organization's specific needs and risk profile, decision-makers can make informed choices about investing in the right cybersecurity measures.The practical implementation of the proposed MOORA-based cybersecurity system is demonstrated using real-world data from a diverse set of organizations. The results showcase the effectiveness of the method in guiding cybersecurity decision-making, leading to the identification of optimal solutions that strike the best balance between performance, cost, and resource allocation.The alternatives are A1 is Providing only essential information and continuing to use the service or product, A2 is Giving wrong or partially wrong information as personal data (misinformation), A3 is Closing the account, disposing of, or deactivating the smart device or application and A4 limiting the use of the application, financial institution, or device. The Evaluation parameters are C1 is Low trust in the firm, device, or application, C2 is Poor referrals or negative word-of-mouth from previous users about the service or app, C3 is Negative previous online experience, C4 is Being tech-savvy, experienced, and knowledgeable about recent trends in data privacy and cybersecurity, C5 is The firm or institution not meeting essential privacy and security expectations, such as privacy policies, notices (cookies), seals, etc and C6 is Perceiving that the benefits outweigh the risks of disclosing information.The final result is Limit the use of application, financial institution or device, etc (A4) is got first rank and Provision of strictly necessary Information and continue the use of service or product (A1) is got lowest rank.
Analysis of the epidemiological profile of patients at the Physiotherapy School Clinic of UNEC - Nanuque

The UNEC-CASU Health Care Center is a school clinic for the Physiotherapy course at the Centro Universitário de Caratinga-UNEC Nanuque higher education institution. It was founded in 2010 and aims to provide practical classes for the Physiotherapy course, as well as the compulsory supervised internship, which takes place from the eighth period of the course. Objective: To verify, through a documentary analysis, information on the epidemiological profile of patients at a physiotherapy school clinic located in the city of Nanuque-MG. Methodology: By means of an exploratory documentary study, this study analyzed the characteristics of the epidemiological profile, as well as the pathologies presented by the patients of a Physiotherapy school clinic, observing factors that interfere positively or negatively both in their health and in the quality of care, with a view to the results obtained through the Physiotherapy treatment carried out by trainees from a higher education institution in the city of Nanuque-MG. Results: It was observed that the population seeks physiotherapy rehabilitation services because they have a greater number of musculoskeletal injuries, portraying difficulties in carrying out everyday activities. Conclusion: It can be concluded that, in general, among the various areas of physiotherapy worked on in physiotherapy clinics, the specialty of orthopaedic and trauma physiotherapy is the most sought-after area in the municipality of Nanuque, Minas Gerais, and that the population seeks physiotherapy rehabilitation services because they have a greater number of musculoskeletal injuries, including difficulties in carrying out their daily tasks.
Detection of Banking Financial Frauds Using Hyper-Parameter Tuning of DL in Cloud Computing Environment

When income, assets, sales, and profits are inflated while expenditures, debts, and losses are artificially lowered, the outcome is a set of fraudulent financial statements (FFS). Manual auditing and inspections are time-consuming, inefficient, and expensive options for spotting these false statements. Auditors will find great assistance from the use of intelligent methods in the analysis of several financial declarations. Now more than ever, victims of financial fraud are at risk since more and more individuals are using the Internet to conduct their financial transactions. And the frauds are getting more complex, evading the protections that banks have put in place. In this paper, we offer a new-fangled method for detecting fraud using NLP models: an ensemble model comprising Feedforward neural networks (FNNs) and Long Short-Term Memories (LSTMs). The Spotted Hyena Optimizer is a unique metaheuristic optimization technique used to choose weights and biases for LSTM (SHO). The proposed method takes inspiration from the law of gravity and is meant to mimic the group dynamics of spotted hyenas. Mathematical models and discussions of the three fundamental phases of SHO — searching for prey, encircling prey, and at-tacking prey — are presented. We build a model of the user’s spending habits and look for suspicious outliers to identify fraud. We do this by using the ensemble mechanism, which helps us predict and make the most of previous trades. Based on our analysis of real-world data, we can confidently say that our model provides superior performance compared to state-of-the-art approaches in a variety of settings, with respect to both precision and.
Replacing IOT Hub with Multiple Block chain for IOT Data Aggregation and Predicting Land Sliding Through Machine Learning Technique

The IoT devices in remote areas are very much vulnerable. The data collecting hub is not secured for recent attacks. The data transferred is attacked before reaching the server. In the existing system, the blockchain is used for data aggregation. The data is secured by this technique but the cost of performing this is very high. In the proposed system the data aggregation is done by using multiple block chains which increase performance as well as the security provided. After receiving the data, the land sliding is predicted with heuristics. The device energy will be consumed very low when compared to the existing system. Security will be high by using multiple chains. The overload of mining and cryptography will be avoided.
Junk Mail Content Detection Using Logistic Regression Algorithm

In contemporary times, things have moved away from traditional method to sophisticated way of communication via social media. One of the common ways information is disseminated amongst people is in the use of emails. Emails are very effective, easy and less costly to use by the sender but invariably costly to the recipient. This is due to the effect unwarranted messages which are thrown in tons are being received daily. This paper focus is on developing an effective junk mail content detector to effectively detect the content of messages and properly classify them thereby eliminate spurious emails. Logistic regression and Random Forest algorithms were employed and the result showed thar our model Logistics regression proves a superior performance.
SUPPORTING STRATEGIES OF THE RECOVERY FOR STUDENTS' LITERACY AND NUMERACY IN ELEMENTARY SCHOOL ENVIRONMENT

The purpose of this study was to obtain an overview of the classroom and school environment as a support for literacy and numeracy recovery. This quantitative descriptive research uses a survey method. The survey was conducted on teachers at SDN Pacing I and SDN Pacing II Sukosewu, Bojonegoro Regency. The data were obtained from the respondents (12 classroom teachers). Six teachers came from SDN Pacing I Sukosewu and six from SDN Pacing II Sukosewu. The instruments used in this study were a questionnaire about the text-rich classroom environment, the text-rich school environment, the numeracy-rich classroom environment, and the numeracy-rich school environment. The data analysis technique in this study used the percentage of answers. The results of this study show that improving strategies for supporting students' literacy and numeracy recovery can be carried out on indicators 1) the books in the library are grouped and arranged by genre, 2) the availability of at least 3 literacy corners and an environment that supports differentiated learning, 3) teachers have conducted research classroom actions related to student literacy, 4) teachers collaborate with other teachers to strengthen literacy in the co-curricular domain, 5) teachers collaborate with other teachers to strengthen literacy in extracurricular areas, 6) there are height/weight measuring devices, thermometers, numerical descriptions of space in the school environment, 7) classrooms are equipped with tools related to numeracy, 8) there is a word wall developed from the learning theme, 9) in the school environment there is a positive message that students can become numerates, and 10) the teacher provides an example in implementing numeracy.
Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound

In obstetric sonography, the quality of acquisition of ultrasound scan video is crucial for accurate (manual or automated) biometric measurement and fetal health assessment. However, the nature of fetal ultrasound involves free-hand probe manipulation and this can make it challenging to capture high-quality videos for fetal biometry, especially for the less-experienced sonographer. Manually checking the quality of acquired videos would be time-consuming, subjective and requires a comprehensive understanding of fetal anatomy. Thus, it would be advantageous to develop an automatic quality assessment method to support video standardisation and improve diagnostic accuracy of video-based analysis. In this paper, we propose a general and purely data-driven video-based quality assessment framework which directly learns a distinguishable feature representation from high-quality ultrasound videos alone, without anatomical annotations. Our solution effectively utilizes both spatial and temporal information of ultrasound videos. The spatio-temporal representation is learned by a bi-directional reconstruction between the video space and the feature space, enhanced by a key-query memory module proposed in the feature space. To further improve performance, two additional modalities are introduced in training which are the sonographer gaze and optical flow derived from the video. Two different clinical quality assessment tasks in fetal ultrasound are considered in our experiments, i.e., measurement of the fetal head circumference and cerebellar diameter; in both of these, low-quality videos are detected by the large reconstruction error in the feature space. Extensive experimental evaluation demonstrates the merits of our approach.
Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation

The use of Shap scores has become widespread in Explainable AI. However, their computation is in general intractable, in particular when done with a black-box classifier, such as neural network. Recent research has unveiled classes of open-box Boolean Circuit classifiers for which Shap can be computed efficiently. We show how to transform binary neural networks into those circuits for efficient Shap computation. We use logic-based knowledge compilation techniques. The performance gain is huge, as we show in the light of our experiments.
Research on the construction of sports statistics index system based on AMOS model

Abstract With the rapid development of the AMOS model, it provides new ideas for the construction research of sports statistical index systems. This paper firstly measures the level of sports development based on the current situation of the construction of a sports statistical index system, and the sports statistical index system is scored by the comprehensive index method. Secondly, it identifies for SEM model and AMOS model and establishes the sports statistical index system based on the AMOS model. Finally, it was verified that the dimensional division of the PE model and variable indexes were reasonable, and the relationship between latent variables was defended on the basis of variable index calculation and utility analysis. The results show that student expectations have the greatest positive total impact on students’ perceived value with a value of 0.58274, that student expectations have the greatest indirect impact on students, and that the indirect impact is close to its direct effect impact with a value of 0.26845, that the total impact on students ranks second with a value of 0.66374, and that student expectations have an indirect impact on student complaints and student loyalty through one and more intermediate variables. Thus, it shows that the AMOS model can effectively solve the problem of constructing a sports statistical index system and promote the development and innovation of research on constructing a sports statistical index system.
Exploring the online interaction model of college English based on deep learning network

Abstract In this paper, we apply a deep learning model to discriminate sentiment in an interactive model of online college English education and propose a fusion model that splices convolutional neural networks and bidirectional long- and short-term memory neural networks horizontally. Convolutional neural networks are good at capturing the sentiment feature vectors using multi-channel convolutional kernels but are unable to extract the sentiment information above and below the sentiment sequence. The short and long-term memory neural network is able to extract the sentiment feature vectors by using recurrent neural networks, which can better compensate for the shortcomings of the convolutional neural networks. The online teaching of college English is selected as the object of analysis, and the improved Flanders interaction analysis system is used to study the online interaction process of college English so as to propose suggestions for the interaction of online teaching of college English. Then the performance of the model is analyzed through simulation experiments. Compared with the traditional TextCNN and BiLSTM , the CNN −BiLSTMATT sentiment analysis model has an accuracy of 0.8611, precision of 0.8471, recall of 0.8691, and F1 of 0.8562, so the CNN − BiLSTMATT sentiment analysis model is more suitable for college English online interaction. This study overcomes the disadvantages of online interaction and continuously improves the efficiency of online teaching interaction.
Research on intelligent recommendation algorithm of literature based on knowledge graph technology

Abstract To address the issue of cold start and data sparsity in recommendation algorithms, this paper introduces a knowledge graph to assist intelligent recommendation algorithms in exploring. Firstly, it is introduced that the introduction of knowledge graphs into recommendation algorithms can introduce more semantic relationships as well as mining higher-order connection relationships, thus improving the purpose and user satisfaction. Second, the three categories of extant recommendation techniques—embedding-based, path-based, and propagation-based—are compiled. We examine how the current methods extract data from entities and connections in the knowledge graph and contrast the benefits and drawbacks of the three different types of approaches. Furthermore, the AUC metrics on the three datasets improve by 2.9%, 1.6%, and 1.2% over the state-of-the-art baseline, respectively, showing the effectiveness of the KGAT-CI model in using collaborative and knowledge-aware information, according to experiments on intelligent algorithms in the literature. It may be determined that the addition of knowledge graphs can improve the present recommendation algorithms’ accuracy, variety, and interpretability.
Analysis of the effect of user-understanding-based retrieval system improvement on the application of intelligent archive management

Abstract As a precipitation and accumulation of history, archives management has gradually tended to be digitalized and informalized with the continuous updating and development of modern technology. In this paper, we first study the retrieval system and focus on the Boolean model, vector model, and probabilistic model in information retrieval technology. The matching relationship between documents and queries is detected from the document set for the user’s query, and a relevance retrieval system based on user understanding is proposed to solve the matching problem. The amount of information that needs to be retrieved is growing exponentially, and how a user perceives the information is crucial to the process. Then, in order to solve the problem of insufficient retrieval efficiency caused by the explosive growth of wisdom files, the retrieval system is creatively optimized on the basis of the ant colony algorithm, which effectively improves the efficiency of wisdom file management. The efficiency of the optimized retrieval system is verified and analyzed in an experimental simulation environment. The findings demonstrate that when the amount of archives rises, the retrieval effectiveness of the improved ant colony algorithm described in this study marginally improves, but in 10~35s. As the inventory of the Smart Archives increases, the content retrieval of the archives will become more and more frequent. This study improves retrieval efficiency and serves as a good demonstration for the construction of archival management information technology.
Optimization of Teacher Incentive Mechanism and Policy Supply in Rural China Based on Judgment Matrix

Abstract With the continuous development of artificial intelligence technology, rural education is crucial in Chinese education, and the problem of rural teachers’ incentive mechanisms directly affects the quality of rural education. Firstly, according to the incentive mechanism of rural teachers, quantitative and qualitative research on the incentive mechanism of teachers in rural areas using the fuzzy evaluation method is proposed. In the fuzzy judgment matrix, each level is determined based on the decomposition of the previous level, and the results of the previous level are determined based on the calculation results of the next level. Then, using the relevant motivation theory and questionnaires, the principal and teachers of the school were investigated to understand the current situation and main problems of the construction of teachers’ motivation mechanism in the junior high school of Un Dong Town, Duyun City. Using the fuzzy evaluation method to analyze the difference between the actual feeling and expected demand of teachers’ motivation, the result shows that the difference between salary and promotion is 1.22 and 1.19, which means that the difference between salary and promotion is bigger in the motivation mechanism of rural teachers, and we should focus on reforming the salary scheme and implementing the promotion training mechanism of rural teachers. This study confronts the problem of rural teachers’ incentive mechanism, which has good theoretical and practical guidance for improving education quality and so on.